Quality-Driven Curation of Remote Sensing Vision-Language Data via Learned Scoring Models
Authors: Dilxat Muhtar
Enzhuo Zhang
Zhenshi Li
Feng Gu
Yanglangxing He
Pengfeng Xiao
Xueliang Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Remote Sensing Image
Vision Language Data
Vision Language Model
Primary area: applications
TL;DR: ScoreRS, a quality assessment model trained on carefully curated remote sensing vision-language preference data. ScoreRS effectively scores and filters vision-language datasets, improving model performance by selecting high-quality data.

Abstract: Vision-Language Models (VLMs) have demonstrated great potential in interpreting remote sensing (RS) images through language-guided semantic. However, the effectiveness of these VLMs critically depends on high-quality image-text training data that captures rich semantic relationships between visual content and language descriptions. Unlike natural images, RS lacks large-scale interleaved image-text pairs from web data, making data collection challenging. While current approaches rely primarily on rule-based methods or flagship VLMs for data synthesis, a systematic framework for automated quality assessment of such synthetically generated RS vision-language data is notably absent. To fill this gap, we propose a novel score model trained on large-scale RS vision-language preference data for automated quality assessment. Our empirical results demonstrate that fine-tuning CLIP or advanced VLMs (e.g., Qwen2-VL) with the top 30% of data ranked by our score model achieves superior accuracy compared to both full-data fine-tuning and CLIP-score-based ranking approaches. Furthermore, we demonstrate applications of our scoring model for reinforcement learning (RL) training and best-of-N (BoN) test-time scaling, enabling significant improvements in VLM performance for RS tasks. Our code, model, and dataset are publicly available.

Quality-Driven Curation of Remote Sensing Vision-Language Data via Learned Scoring Models
Dilxat Muhtar
Enzhuo Zhang
Zhenshi Li
Feng Gu
Yanglangxing He
Pengfeng Xiao
Xueliang Zhang*
Nanjing University
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Abstract
Vision-Language Models (VLMs) have demonstrated great potential in interpreting remote sensing (RS) images through language-guided semantic. However, the effectiveness these VLMs critically depends on high-quality image-text training data that captures rich semantic relationships between visual content and language descriptions. Unlike natural images, RS lacks large-scale interleaved image-text pairs from web data, making data collection challenging. While current approaches rely primarily on rule-based methods or flagship VLMs for data synthesis, a systematic framework for automated quality assessment of such synthetically generated RS vision-language data is notably absent. To fill this gap, we propose a novel score model trained on large-scale RS vision-languee automated quality assessment. Our empirical results demonstrate that fine-tuning CLIP or advanced VLMs (e.g.. Qwen2-VL) with the top 30% of data ranked by score model achieves superior accuracy compared to both full-data fine-tuning and ELIP-score-basse ranking approaches. Furthermore, we demonstrate applications of our scoring model for reinforcement learning (RL) training and best-of-N (BoN) test-time scaling, enabling significant improvements in VLM performance for RS tasks. Our code, model, and dataset are publicly available
preference data for
Introduction
The advancement of artificial intelligence has consistently benefited from scaling across three crucial dimensions: data volume, computational resources, and model complexity [26, [2]]. In visual understanding, vision-language models (VLMs) have particularly benefited from diverse training data, exhibiting evolutionary patterns that mirror human cognitive development in perception and interpretation. From the foundational CLIP model
46 to recent integrations with large language models (LLMss) 8 61 (5),, VLMSs have achieved remarkable success, emerging as indispensable tools for weal-world applications ranging from agents system [81] to autonomous driving [43.
Despite these ad advacemments, VLMs demonstrate limited capability in interpreting remote sensing (RS) images due substantial domain shifts in data distributions 31 27). This performance gap stems primarily from critical bottleneck: the scarcity of large-scale vision-language data for RS domains, which, unlike natural images, lacks the abundant, naturally aligned image-text pairs readily
"Corresponding Author
2
Data
Model
Code
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Examples from LHRS-Align dataset
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Figure 1: Examples from RS vision- vision-language datasets showing quality issues across five dimensions.
Green represents reasonably good expression, while red represents low-quality expression
available through web crawling. Current approaches to bridge this gap include: (1) binary classi- fication models filtering RS data from general vision-language datasets like DataComp 79 (2)
rule-based construction of vision-language pairs using OpenStreetMap tags [62], and (3) automated
or leveraging supervision from internet images [38]. While these methods advance holistic RS understanding, they introduce significant qual- ity concerns: aale-based approaches often produce informationally sparse or semantically inconsistent pairs, while VLM "LM--ennrated data risks propagating hallucinatory content that misrepresents the actual imagery and incorrectly infers answers questions that cannot be determined from the given images (Figure D. The absence of robust, automated quality assessment framework consequently constrains further improvements RS-specific VL.Ms (30
data generation through flagship VLMs [36 27
39
30
To address this critical gap. our method commences with the definition of text quality for RS images, outlining five crucial dimensions that characterize high-quality descriptions or instruction samples: relevance to visual content [44 (6]] specificity and detail [30 29
83 completeness of coverage 83 clarity and fluency of language 62 and semantic richness for RS
for salient features
applications 63 52 Guided by these quality dimensions, we construct preference datasets for both image-captions and vision instructions by employing diverse array of policy models innludding RS RS-specialized VLMs and open- soen-source alternatives while utilizing a combination of rule- based evaluation metrics and flagship VLMS as objective judges. Building upon these curated preference datasets, we introduce ScoreRS, a novel learned quality scoring model tailored for RS vision- vision-language data, developed via a three-stage progressive training recipe. We evaluate ScoreRS's s effectiveness in both data quality ranking and its practical applications a large VLM reward model for group relative policy optimization (GRPO) (51) reinforcement learning (RL) and best-of-n (BoN) selector. Our results demonstrate that fine-tuning CLIP and Qwen2VL [61] on just 30% of the data, ranked using ScoreRS scores, outperforms models trained on either the complete dataset or CLIP-score ranked data. Furthermore, evaluation the challenging RS-specific vision-languan
benchmark
VG-DIOR (74] and LHRS-Bena [39 reveals that ScoreRS can be integrated with rule-based rewards effective BoN selector for improving
for GRPO RL to enhance model capabilities, and serves results when scaling VLMS with multiple generated samples at test time.
The main contributions of our work can be summarized as follows:
1. We establish framework for defining text quality in RS vision-language data and introduce the first large-scale RS RS-sciicfic preference dataset, comprising pairwise preference pairs for both image-captions and vision instructions. Based on this dataset, we develop ScoreRS

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novel data scoring model specifically designed for automated quality assessment of RS vision-language data.
2. We demonstrate that models fine-tuned on just the top 30% of RS vision-language data ranked by ScoreRS scores consistently outperform those trained either the complete dataset or data ranked using CLIP scores, highlighting the efficacy of our quality-driven curation approach.
3. We validate ScoreRS's s effectiveness through two practical applications: (1) as a reward model for GRPO-based RL, and (2) BoN selector for VLLM-generan samples at test time. Both applications yield improvements RS VLM performance across multiple challenging benchmarks.
2 Related Work
2.1 RS Vision-Language Data Curation
Unlike the general vision domain, where lasion-language data can be readily crawled from abundant image-text interleaved webpages [69
71. the RS domain presents challenge. While RS lacks corresponding text descriptions and analytical conversations about these images. To harness the potential of VLMs in the RS domain, existing studies have primarily focused on two approaches for data curation: rule-based methods and synthetic data generation using established VLMs. For tule-baeed approaches, RS5M [79] explores the use of binary RS image classifiers to select RS image-text pairs from open-source general vision-langee Laniuag datasets. Similarly, RemoteCLIP [31]. Skyscript [62]. and SkysenseGPT [36] implement manually designed text templates or relation graphs for data construction. In parallel, studies such as VHM
16
57
48
rich open-source image data [39 40
54
[4]. LHRS-
Bot [39]] LLHRS-Bot-Nl 60], and GeoChat 277 leverage flagship VLMs like GPT4 Gemini for synthetic vision-language data curation. While these works have advanced the development of RS RS-specific VLMs, our practical applications reveal that data quality remains suboptimal, motivating the development of automated data selection methods.
2.2
RS VLMs
The remarkable success of VLMs in understanding images and engaging in complex tasks 35
43
has inspired the development of specialized VLMs for RS image interpretation. Several approaches have focused on adapting CLIP [66 for the RS domain. RemoteCLIP [1]. Skyscript 662, and RS5M fine-tuned CL.PP using carefully curated RS image-caption pairs, enabling robust RS zero-shot classification and image-text retrieval capabilities. For larger VLMs, GeoChat (27] pioneered the fine- tuning of LLaVA [32 with RS-specific vision instruction datasets. Subsequent works have expanded this paradigm through various strategies: incorporating volunteer geographic information (VGI) [39] integrating high-quality vision-language data (30 44), modeling relational graphs [36), implementing ensembled vision encoders for vision-centric designs 78), improving temporal understanding of RS
79
images [24], and scaling data volume with significantly smaller encoder-decoder architectures [7]]. The versatile and successful applications of RS VLMs have directed our attention to the fundamental building blocks underlying these models: data quality and utilization. Rather than proposing new architectural designs, our work focuses on addressing how to improve data quality and enhance VL.M performance within existing frameworks.
2.3 Parameter-Wist Data Selection
As data volume continues to expand and data complexity evolves, quality control has become increasingly challenging. Beyond traditional rule-based data selection methods [53 64 researchers have shifted focus toward parameter-wis wise selection approaches for more sophisticated data curation. In the language domain, methods such as QaRater [65] and LESS (68) explore training dedicated scoring models or leveraging model gradients to select high-quality data for LLM pretraining and instruction tuning. Similarly, for languaee data, Zhang et al. [77] investigated scoring models for curating image-text pairs. The model selection approach has been widely adopted in recent state-of-the-e VI.Ms, including I.aama-.2 2- Vision [8]. Deepseek [66), and Qwen2VI. [61]. all of which implement scoring models for data filtering. Despite these advances, there is notable
3

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Image-Caption Preference Dataset
Viston Instraction Preference
Training and Downstream Application
Figure 2: Pipeline for generating pairwise preference datasets and the training/application of our ScoreRS model. I, represents RS image, and T; represents an image caption, question, or conversation associated with the image
absence of open-source scoring models specifically designed to evaluate the quality of RS vision- language data. In this work, we develop and release the first open-sourg scoring model tailored for RS vision-language data quality assessment.
Method
To establish quality control for RS vision-language datasets, we propose scoring framework that quantitatively evaluates data quality through learned model. This framework enables data curation through score-based ranking and selection of high-quality samples.
Formally, we define a scoring function fo T x + R. parameterized by 0, where T and I denote the text and image spaces, respectively. Given an image-text pair (I,7") E I x T, which may consist either simple caption or a multi-turn conversation associated with the image, the model outputs scalar quality score TT) The scoring function is trained assign higher scores to better-al better-aligned image-text pairs (I, through pairwise preference learning. For each image we collect pairs of text (T -) where T is preferred over T This preference dataset is formally defined as D = (I, T,t T..T | The pairwise preferences are modeled using the Bradley-Terry model 3 which defines the probability of T being preferred over T given image / as:
exp(fo(I,T*)) exp(fe(1,Tt)) + exp(fo(1,TT))
p(T (T)
The parameters 0 are optimized by minimizing the empirical negative log- ligeliied loss 42
59
(())= EE . wD (fa, - fo(,
where (-) denotes the sigmoid function.
Given the absence of RSS-peccfic vision-language preference datasets, we begin by establishing framework that defines five key dimensions of textual quality for RS images. This framework characterizes what constitutes high-quality descriptions and question-answer pairs, providing the foundation for data collection pipeline construct both image-caption and vision instruction preference pairs (Figure 2). Subsequently, we detail the construction of our scoring function fo by leveraging pre-trained VLMs and employing progressive training strategy.
3.1
Preference Data Construction
3.2 Quality Dimensions for RS Vision-Language Data
To develop effective scoring function fe. we must first establish clear criteria for what constitutes high-quality RS vision- laniuue data. These criteria provide the foundation for constructing our preference dataset and subsequently training score function.
To this end, we define five critical dimensions that characterize high-quality RS vision-language data:
relevance [44] 36) 700, specificity & detail (30 29 83,, completeness 4 83), clarity & ffuency [62].
and semantic richness [63] [2]. For each dimension, we establish a 5-point scoring system (I=Poor
to 5=Exxellent) with distinct criteria for both image-caption pairs and instruction samples. Detailed
descriptions of these quality dimensions, along with illustrative examples and the complete scoring rubrics, are provided in the Appendix

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3.2.1 Image-Caption Preference Dataset
Following established practices in VLM training 61
begin by constructing an image- caption preference dataset to train ScoreRS to evaluate caption quality for RS images. Given the geographical variations in RS images [3 62 we utilize the AHRgnAlign dataset [39] as our image source, This dataset contains 1.15M orthorectified RS images from major global urban areas, enabling ScoreRS learn from diverse geographical contexts worldwide. Prior to generating preferences, we implement a rigorous image deduplication process to ensure image quality and representativeness resulting 76K distinctive RS images. The detailed method for this deduplication process provided in Appendix B.
39
44
32
For each deduplicated image, we generated captions using three VLMs: RS- RS-specific LHRS-Bot- Nova [30] and general-purpose Qwen2VI.-7B [6]] and InternVL.-2.5-888 [8. The rationale for selecting these generation models detailed Appendix B.2 We generate captions for each image using same prompts and sampling parameters across all models. The resulting captions are then evaluated by GPT-4o based on our predefined scoring system. For each image, we compute the mean score across five dimensions per caption, designating the highest-scoring positive (T*)) and others as negative (T" After removing results with parsing errors, we construct a dataset of 72K image-caption preference pairs.
To validate GPT-4o's reliability as a preference judge, we conduct human evaluation study on 1,000 random pairs. Human experts independently annotate these samples to identify positive and negative captions. The results show 92.6% agreement (926 out of 1,000 samples) between human judgments and GPT-4o's 's assessments. This high level of agreement validates our choice of GPT-40 as reliable judge for generating pairwise preferences.
3.2.2 Vision Instruction Preference Dataset
Vision instruction data consists of conversations about images 32 To construct a RS-specific vision instruction preference dataset that enables ScoreRS to evaluate responses to diverse user queries, we prioritize collecting a broad spectrum of question types and conversation scenarios. We aggregate vision instruction data from multiple sources: GeoChat [27] (306K samples), (39.8K samples), and a subset of SkysenseGPT (36) (381K samples) as the source of our vision instruction preference dataset. To address potential redundancy across these datasets, we implement two-stage similarity-bast -ased filtering process, resulting in 1 12K diverse and non- non-redundant instruction samples. The detailed method for this deduplication procedure is provided in the Appendix [B.1]
[39]
After deduplication, we categorize conversations into close-ended questions (with definitive answers) and open-ended questions (without verifiable answers). For close-ended questions, we prompt Qwen2V1.-BB to generate an answer ÿ. When ý differed from the provided answer y, we extract the sample, treating ý negative target T" and y as positive target T
and Qwen2VL-7B generate answers ÿ1 and J2. then prompt Qwen2VL-72B to evaluate these along with the source dataset answer y using predefined dimensions. We designate the higher scoring answer as the positive target (T+) and others as negative targets (T-). We do not directly consider the answer from the source dataset as T because many of them are generated by older VLMs and are often too short, meaningless, or contain incorrect statements. To manage costs associated with the large volume of data, we employ Qwen2VL--72B rather than GPT-4o as our evaluator. After filtering out parsing errors and applying human-defined quality rules, we curate dataset of 26K vision instruction preference pairs. The detailed prompts used in this process are provided in Appendix B.3.2 While collecting question set Q, we notice most sources lack RS application-sspecfic questions (c.g., agricultural and disaster analysis). To enhance ScoreRS's s domain-speeitfe performance, we create a specialized set of five manually crafted questions for each of 12 expert-defined RS image analysis categories. We then randomly sample 35K RS images from our deduplicated 12K image dataset. For each image, we randomly select a question from the manually designed question set and prompt Qwen2.5- 13B to rephrase the question to increasing the diversity. LHRS-Bot-Nova and Qwen2VI. Wwen2VLL-BB then generate answers based on these image-question pairs. Finally, GPT-4o evaluates the generated answers, selecting the higher-scoring one as the positive target T+ and treating others as negative targets T-, This process yield 33K RS-specific vision instruction preference data points
For open-ended questions, we use
5

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after removing entries with parsing errors. The manually designed questions and categories can be found in Appendix B.3.2
3.3 Training and Application
Training Our ScoreRS model is initialized with Qwen2VL-7B, with the language head replaced by linear layer to output scalar score, following the standard value-head-based reward model (59) 14] We do not consider generative score modeling due to efficiency concerns. Similar to the standard recipe for training large VLMs 61 32,, we implement a multi-stage training procedure gradually train ScoreRS to distinguish better rision-language pair. In the first stage, we train the newly introduced value head using a pure text preference dataset, UltraFeedback [[2]. to provide a good initialization for the value head. Then, we unfreeze the ViT and value head and train ScoreRS on our image-caption preference dataset to enable ScoreRS to better understand RS images. Finally, we unfreeze the LLM and conduct full-parameter training with our vision instruction preference dataset and the additional RLHF-V (73] dataset. This training approach enables ScoreRS to effectively identify high-quality outputs and assign higher scores to better responses across diverse RS scenarios.
Application Beyond data selection, we also explore ScoreRS's usage for RL training and BoN selection. We primarily discuss implementing ScoreRS for RL, its applications data selection and BoN selection are straightforward. Our RL framework is based on GRPO [51], chosen for its computational efficiency and ease of hyperparameter tuning. RL training methods typically require a reward model to evaluate each action trajectory and update the model parameters to favor outputs with higher scores (i.e.. responses more aligned with human preferences). While DeepSeek- RI [19] demonstrated that rule-based rewards for close-ended questions with verifiable answers are effective for RL training, this approach is insufficient for RS applications. In the RS image understanding domain, most questions are open-ended, such as "Describe the urban development patterns visible in this satellite imagery", requiring nuanced interpretations rather than definitive answers. These questions also usually do not have veriftable answers, as different interpretations can lead to multiple acceptable responses. To address this challenge, we introduce a novel reward method using ScoreRS for evaluating open-ended responses while incorporating rule-based rewards for close-ended questions, creating an approach that handles both question types. Specially, for close-ended questions, we employ binary reward (0 or 1) based on exact match or intersection over union (IoU) with the ground truth. For open-ended questions, where we have a reference answer (typically sampled from standard vision instruction datasets), compute the reward for predicted answer ÿ using the following formulation:
1 -
- jo(x)) x 8), if fa( > So(y)
otherwise
where B > O is hyperparameter controlling the reward sharpness. We use this reference-based approach because accelcrates learning. The reason behind this approach is that the reference answer serves as a baseline, allowing the policy model to improve upon it by generating responses that receive higher scores. This method focuses on continuous improvement rather than selecting the least problematic option from multiple suboptimal alternatives that may be worse than the reference answer itself. We give our detailed unsuccessful attempts and our reasoning in Appendix
F2
4 Experiment
We evaluate ScoreRS's effectiveness across three key applications: (I) vasion-language data selection for training VLMs, (2) RL training, and (3) BoN selector. We also analyze the impact of score model's size, initialization, and training strategies, along with our curated preference dataset quality.
4.1
Vision-Language Data Selection
4.1.1 CLIP Finetuning
Experimental Setting To validate ScoreRS's effectiveness for data selection, we score and rank training samples from RemoteCLIP 31 to finetune CLIP-VT--/14 [46). We compare against three baselines: (1) CLIP without finetuning, (2) CLIP finetuned on the complete dataset, and (3) CLIP finetuned on CLIP-score filtered data. We provide detailed training hyperparameters in

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Table 1: Comparison of finetuned CLIP models classification tasks. Top-1 (@1) and top-5 @ () classification accuracies are reported.
NMJUMI NWPUMS
EavdAl W1
EandAlis
A4361
AIDMS
WIF-MSING
WIR-A505
Arg
GLI
60:33
09:39
13.14
68:33
30,8
03:31
58.1
8517
86.34
01.55
06.37
86:44
16.14
960
9922
06.66
Recoactt.D C1D Sear)
28.56
03:33
0:49
58.05
96.25
30.0
9813
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1938
202
68.0
13.1
15.0
18.50
1808
Table 2: Comparison of finetuned CLIP models on cross-modal retrieval tasks. Text-to-image (T2I) and image-to-text (12T) performance shown using top-1 recall (R @ 1) and top-5 recall (R @5)
BAKI 17
Re
RAITD RIs
KARCD RO1
Aeg Ril
Neg kir1
CLIP Reneact (all)
21.4
06:54
36.01
$3.24
3:31
17.00
3.5
11.50
13:18
43:35
$6.63
80.11
242
2161
HesekKLIP (30% Scasdlsy
44.59
8200
$7.00
$0.80
1350
3100
5.9
2428
27.31
55.35
Appendix D.3 and detailed description of evaluation methodology. including datasets and metrics, in Appendix D.3.I Moreover, we also compare using ScoreRS for data filtering with filtering with more advanced VLMs like SigLIP-2 [58] and domain specific VLMs like CLIP-LION-RS (62 in
Appendix C.3.3
We evaluate the finetuned models on classification and retrieval tasks. Results in Table 1 and Tableeehal the "more data yields better performance" assumption for RS-specifii VLMs. CLIP-score filtering improves performance by 5% (classification) and 1% (retrieval), while our ScoreRS filtering further advances these gains to 7% and 2% respectively, outperforming both complete dataset and CLIP-score filtering. These findings confirm our hypothesis that current RS vision-language data are suboptimal and require quality control.
Main Result
We further evaluate different parameter-wise data selection methods across various thresholds (Figure B Our analysis reveals task-spec patterns: retrieval tasks show higher sensitivity to noise, with greater improvements under stricter filtering, while classification tasks maintain reasonable performance with more relaxed criteria. Notably, quality filtering consistently improves model performance, aligning with previous research that highlights the erucial role of high-quality data in CLIP-style pretraining [69]. More discussion these results and further comparison in even more extreme data filtering scenarios can be found in Appendix F.I and C33.2
Classification
Retrieval
27.5
Accuracye
28.5
22.
25.
CLi-- Gcare
24.
Dalaset
Figure Classification and retrieval results using differ- ent percentages of data selected by SL.P-Scorre and Scor- eRS. Top-1 (@ () results shown as average scores across all datasets
4.1.2 Large VLMs Finetuning
Experimental Setting We fine-tune Qwen2VLL-7B [6] using RS image-caption data and vision instruction datasets from VHM 44). Following standard VLM training practices, we use two-stage approach: first training only the vision vision-language bridge layer with pretrained data, then training the LLM with vision instructions. We evaluate ScoreRS's effectiveness in selecting high-quality data for both stages, comparing against models trained on the complete dataset and data filtered by LongCLIP-L (75] which we chose over original CLIP for its superior handling of longer captions and conversations. To reduce computational costs, implement LoRA [2 with rank for LLM finetuning. Detailed configurations are provided in Appendix D.4
Main Result We evaluate our finetuned model on multiple RS tasks (image classification, visual grounding. question answering) and assess general RS knowledge using the challenging LHRS- Bench [39]. As shown in 3 filtering mapge-caption data with ScoreRS and retaining the top 30% achieves best performance, yielding 1% improvement on BERS-Bench. Given that the VHM image-caption dataset is synthetically generated by Gemini-Flash, this high ranking threshold further supports our assertion regarding the importance of quality control for RS synthetic data. We further explore the application ScoreRS for filtering vision instruction data. Since VHM vision instruction datasct derives from standard RS benchmarks, applying aggressive filtering (c.g.. 30%) performs
7

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Table 3: Comparison of different data filtering methods and selection strategies. PX indicates the selection of top X% of X% of vision instruction data in the second stage
data in the first stage, while SX represents the selection of top
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Table 4: Comparison between our finetuned model (Qwen2V1--7B-RS) and existing VLMs. Our model trained on quality-filtered data comprising the top 30% of pretraining samples and 60% of instruction samples from the VHM datasets, selected using ScoreRS as the quality assessment model
KK
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64.50
57.17
59.17
$7.56
80390
65.35
75.30
55.80
73.18
16.21
72.18
75.13
15.39
55.35
65.13
01.34
52:35
10.09
$1.40
10.06
73.60
55.30
15:47
02.00
72.10
09.57
11.87
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22:04
3337
1231
75:20
11.35
39:38
37.80
38.11
13339
7230
$3.33
3131
12.45
VHM
92/03
14.33
94.56
10.52
96.50
88.65
$3.30
58.30
90.13
59.09
$7.00
83.52
55.09
33.04
85.16
40000
50005
68.38
05.30
76.42
$4.20
70.50
92.11
10.22
95.00
55.43
12.87
36:33
Qw6x2VL-28-85
94,42
21.99
96.30
$6.59
$7.30
75.80
21.35
30000
96.45
67.85
slightly worse on classification than using the complete dataset. However, when adopting a more moderate filtering approach (e.g., 60%), observe substantial improvements: 3% on classification tasks, 5% on vision question answering, and 0.36% on challenging grounding and CHRS-Beend benchmarks. Notably, ScoreRS consistently outperforms CLIP score-based selection across all evaluation scenarios.
Based on these empirical findings, we strategically select the highest-scoring 30% of pretraining data and 60% of vision instruction data as ranked by ScoreRS, while scaling the LoRA rank size to 128. We then evaluate our finetuned model against leading RS-specific VLMs and state-of-the-ar general-purpose VLMs. As shown in Table 4 our model, trained on high-quality data selected by ScoreRS, not only achieves comparable or superior performance on classification, visual question answering, and visual grounding tasks compared to RS-specific VLMs, but also outperforms general- purpose models on the LHRS-Bench benchmark. These results further confirm that data quality in RS vision-language pairs represents the primary bottleneck limiting the full potential of VLMs for RS image understanding. More discussion of these results can be found in the Appendix
4.2 Reinforcement Learning
We explore using ScoreRS as a reward model for RL training of our Qwen2VL-7B-RR model, using 8K samples (4K open-ended, 4K close-ended) from the filtered VHM vision instruction dataset. Inspired by Deepseek-RI tokens (<think>, </think> for reasoning: <answer>, </answer> for responses). Our reward functions include binary rewards for close-ended questions, ScoreRS-based rewards for open-ended questions (Section 3.3), and format rewards for proper structuring. We evaluate on vision grounding and -HRS-Bench tasks, which better assess RS image understanding capabilities than simpler tasks, and an RL version without ScoreRS rewards
[19]. we implement a two-step reasoning process with special
comparing against -o-raind Qwen2VL-7BB (where we substitute 4K additional close-ended questions to maintain equal training steps). Detailed settings are presented in Appendix D.3)
Table shows that direct RL training on Qwen2VL-77B-RS (Qwen2VI.-7B. RS-Zero) improves performance by 0.2% on LHRS-Bench and 1% on VG- DIOR. Notably, using ScoreRS for re- ward calculation outperforms the vari- ant without ScoreRS-based rewards, confirming ScoreRS's effectiveness as reward model. We also provide a comparison of this training paradigm with DPO and compare it with using GPT-4o as a reward model for open- ended questions in Appendix C.4
Table 5: Comparison of RL-trained models. Qwen2VL- 7B-RS-Zero: Directly apply RI. to Qwen2VL-7B-RS Qwen2VL-7B-RS--3
Qwen2VL-7B-Br fine-tuned with our manually generated reasoning data. Qwen2VL-7B RI: RL applied to Dwen22L--BBRRSSSI "w/o ScoreRS": variant trained without ScoreRS- -ased rewards
VG-DIOR
LHRS-Bench
Qwen2VL-78-4 Qwen2VT--7B-RB RR--NNT--------- (wlo ScoreRS)
58.34
67.08
58.66
66.05
QwenZVI 7B RS Zero
59.64
67.21
Twen2VL-7B-RS-SE
59.21
66.34
Qwea2VL-78- ----R--------II----- (wlo ScoreRS)
62.47
63.71
Qwen2VL-78-RR-Rl
64.52
69.13
8

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During experiments, we observe that Qwen2VL-7B-RS. R-Ro- exhibits overly simplistic reasoning patterns. To maximize RL training benefits, we implement a multi-stage approach: first, we used Owwn22V.-77B-RS-7720 to answer our RS-specific questions from Section 3.2.2 then manually refined these responses to create 2K curated reasoning-answer pairs. We fine-tune Owen2VL--7B-RS on this dataset before applying RL training with the same data used for Qwen2VL-7B- significant wereeementt-gginnnng over 5% on VG-DIOR and 2% on LHRS- Beee--ennhhhhhe also outperforming variants trained without ScoreRS-based rewards. Analysis confirms more reasonable reasoning patterns, with conversation examples in Appendix 13
The resulting model, Qwen2VL-7B-B RS-RR, shows
4.3 Best-of-N Selection
We compare our ScoreRS and CLIP-score with LongCLIP 75 as a BoN selector Specifically, for each question, we generate multiple candidate answers and select the highest-scoring answer with given selector. We utilize three models for answer generation: the RS-specific LHRS-Bot-Nova, the general- purpose Qwen2VL--7B, and our fine-tuned Qwwe22V.-7B-RS--R
We evaluate both VG-DIOR and -eenh datasets. Due to computational constraints and the large volume of the VG-DIOR dataset, we sample the first 1,000 instances for evaluation reduce experimental costs. For all the answer generation, we set the sampling temperature and top-p parameters to 0.95 and I, respectively,
Figure4 demonsttates that using Scor- eRS as selector consistently hances evaluation performance across models for both complex perception (VG-DIOR) and holistic vision under- standing (.HRS-Bench) tasks, while CLIP-score underperforms in these scenarios. Notably, with ScoreRS as the BoN selector, accuracy on the chal- lenging LHRS-Bench dataset exceeds 70% for the first time, highlighting ScoreRS's potential not only for data selection but also for test-time scaling of base models in RS image understanding. We provide additional model validations and comparisons with majority voting in Appendix C.5
VG-DIOR
LHRS-Bench
Accuracy
55
50
Candidale samplas
Candidate samples
LIMCE Muichearr Beaeiti
LoNC MurhNoaae duarss
Figure 4: Comparison with different BoN selectors.
4.4 Ablation Analysis
We validate our multi-stage training strategy and preference dataset quality through ablation studies on held-out set of 6K samples (2K from image-caption preferences, 4K from vision instruction preferences). Performance is measured by accuracy, defined as the scoring model correctly assigning higher scores preferred texts (I*)) over their counterparts (T")). Additional ablation analyses examining score model size and the benefits of RS-specific VLM initialization are provided in the Appendix C.2.
We validate our preference dataset's effectiveness through joint training ex- periments comprising two stages: (1)
Table Ablation study on multi-stage training strategy and preference dataset composition. We evaluate each dataset component's contribution (ICPD Image-Caption Prefer- ence Dataset, VIPD = Vision Instruction Preference Dataset) and demonstrate our multi-stage approach's advantages over joint training alternatives.
initializing the value head with pure text preferences for stability, and (2)
unfreezing both ViT and LLM compo- nents. As shown in Table 6 training with our complete preference dataset achieves the highest reward accuracy. Notably, omitting any subset degrades performance even when evaluating on different domain (e.g., excluding image-caption preferences reduces accuracy on the vision instruction evaluation subset). Our multi-stage training strategy further improves reward accuracy by over 10%
Accuracy ICPD
Accuracy VIPD
Accunacy
Jointy Training
83.13
$1.07
$2.09
x Insage-Captio P.D.
59.44
76.30
67.87
Vision Instraction P.D.
60.17
$1.85
56.03
Multi-Sage Training
92.91
92.97
compared to joint training. These results confirm both the quality of our curated preference dataset and the benefits of our training approach.

---

Conclusion
Vision-language data serves as the fundamental building block for training VLMs. However, the quality control and data curation for RS-specific VLMs have not been fully addressed. In this study, we explore the development of parameter-wise scoring models for high-quality data selection. Through careful construction of RS preference datasets and the training of our ScoreRS model, we demonstrate that current vision- lision-laguuage datasets are far from optimal. Our findings show that using just 30% of quality-filtered data achieves superior performance compared to the complete dataset in both CLIP training and large VIM finetuning. Furthermore, we investigate the application of ScoreRS in RL training and BoN selections. Both applications demonstrate that ScoreRS can enhance VLM capabilities in solving complex and challenging tasks, We anticipate that this study will encourage the RS community to place more emphasis on vision-language data quality control and the strategic utilization of high-quality data.
6 Acknowledgement
This study was supported by the National Natural Science Foundation of China (Grant No. 425221 12), the Natural Science Foundation of Jiangsu Province (Grant No. BK20250065), and the Al and Al for Science Project of Nanjing University (Grant No. 020914380171), We also would like to thank Haoqin Tu, Zhuo Zheng, and Mengke Zhu for their valuable discussions and feedback.
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Justification: Our paper does not rely on strict theoretical assumptions. Quality issues indeed exist in each dataset, as illustrated in Figure For the construction our scoring system, we built it based on established literature.
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Appendices
Quality Dimension and Scoring System
A.1 Quality Dimension
Scoring System
B
Additional Detail for Preference Data Generation
Deduplication
B
2
Model Selection
B.3
Generation Parameters and Prompts
B.3.1
Image-Caption Preference Dataset
B.3.2
Vision Instruction Preference Dataset
B.4
Further Human Validation of Preference Data
Additional Resulf
Score Model Training
Ablation Study
C.2.1
Inflnence of Model Size
C.2.2
Influence of Model Initialization
CLIP Training
C.3.1
Evaluation on Skyscript Dataset
8
C33.2
Evaluation on Extreme Data Filtering Scenarios
C.3.
Comparison with the Filtering Methods
C.4
RI. Comparison
C.4.1
Compare with DPO
C.4.2
Compare with GPT-4o as Reward Model
10
BoN Selection
10
C.6 PPO Training
10
D
Experimental Setting
11
D. Hardware and Framework
11
D.2 ScorekS Training
12
D.3
CLIP Finetuning
12
D.3.1 Evaluation Setting
12
D.4
Large VLMs Finetuning
13
D.4.i Evaluation Setting
14
D.5
GRPO Training
14
Qualitative Example
15
Ranked Data Comparison
15
E.2
Qwen2VL-7B-RR- Inference Example
15

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Discussion nand Limitation
15
F1
CLIP-Score ScoreRS?
15
Reasoning for Reference Based Reward Calculation
15
RS Vision-Language Data Quality
16
Expectation for Building RS- RS-Specific VLMS
16

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A Quality Dimension and Scoring System
A.1 Quality Dimension
The detailed descriptions and clarifications for each dimension are provided below:
The text accurately describes the content visible in the remote sensing image. It does not introduce objects, features, or relationships that are not present (i.e., avoids hallucination). All key elements mentioned in the text are verifiable the image.
Relevance:
Specificity Detail:
The text provides specific and detailed information rather than being vague or overly general. It names identifiable objects, describes their characteristics (e.g., size, shape. color if discernible and relevant), and their spatial relationships.
Completeness:
The text covers the most salient and important features of the image relevant to common RS tasks (e.g.. land use classification, object detection, change detection). doesn' 't omit obvious, critical elements. For question grestion-answering the answer should address the question if the information present.
The text is grammatically correct, well-structured, and easily understandable. uses clear, complete, and unambiguous language.
Clarify & Fluency:
Semantic Richness:
The text captures semantic information that is particularly useful for downstream RS applications. This could include types of land cover (e.g., "deciduous forest," "industrial zone," "low-density residential"). specific object classes ("solar farm," " "roundabout, "cooling towers") "), or activities ("active construction site").
Distinction Between Specificity & Detail, Completeness, and Semantic Richness: We delineated three potentially overlapping quality dimensions ensure non-redundant evaluation:
(1) Specificity & detail focuses on the level of precise information provided (e.g.. "high-rise commer- cial buildings" vs. just "buildings"). It's about granularity and precision in what is described.
(2) Completeness focuses on coverage of all important elements in the image. It's about breadth whether all significant features are mentioned, regardless of how detailed each mention is.
(3) Semantic Richness focuses the of domain-speciff terminology and concepts relevant to remote sensing (e.g., "center-pivot irrigation" " vs. "circular fields").
These dimensions can vary independently-a description might be highly specific yet incomplete, complete yet lacking domain terminology, or semantically rich yet insufficiently detailed justiffig their separate evaluation.
A.2 Scoring System
We provide our detailed scoring criteria for image-caption pairs Table 16 and for instruction samples in Tables 17 and 18
B
Additional Detail for Preference Data Generation
B.1 Deduplication
Image-Caption: We implement rigorous image deduplication process for the LHRS-Align 39 to ensure image representativeness for building our image-caption preference dataset: (1) Feature extraction using the SSCD copy detection model [55 18
compute image embeddings. (2) Similarity computation through cosine distance computation between image embeddings, with carefully tuned threshold of 0.65 (empirically determined through experiments across the range 0.6-0.9). (3) Duplicate grouping via connected-component algorithm, preserving one image-text pair per component. Finally, this deduplication pipeline yields 76K distinctive representative RS images.
Vision Instruction: We employ a two-stage similarity-basse filtering process to ensure diversity of instruction sample: (1) Text similarity: let Q = (Ti, T2, ..., Tr) the set of all questions extracted from the conversations. We compute their semantic embeddings using the BGE (6) model, where for

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(2) Image similarity: For query pairs where ex (T, > 0.65, further examine their corresponding images L; 1, using
any two queries T3 E Q sim TT)
SSCD 45 embeddings: simimage [t [ cos(SSCD (7,) SS)D (S) When both sim (7; Tj)
and siminage (Ti, 1 exceed 0.65, we retain only one question-image sample from the pair minimize
redundancy.
B.2
Model Selection
For the preference generation policy models, aimed maximize diversity. However, considering the high costs associated with adding multiple models (including generation and evaluation costs), we selected policy models for image-caption preference dataset generation and 2 for vision instruction preference dataset creation.
Regarding model families, ensured inclusion of two model types: RS-specific VLMs and general VLMs, to increase answer diversity. For example, RS-specific VLMs typically include domain- specific terminology such "high resolution, true color", while general VLMs often add contextual descriptors like "aerial, satellite".
For RS-specific VLMs, selected LHRS-Bot-Non preliminary evaluations. For general VLMs, we aim to select the highest-performin available models. At the time of dataset preparation, the best open-sourcs VLMs are Qwen2VL and Intern VL2.5, selected these model familics using the same parameter scale (7B) as generation.
30 based on its superior performance in our
for policy
We acknowledge that model size, model family, prompting strategies, and generation hyperparameters all influence the final preference dataset [29] [33 and synthetic data generation itself represents critical research topic 41 strategy while emphasizing diversity and domain specificity of the RS images and instructions used to generate preference data. We leave for future work the exploration how different policy models affect preference data quality and scoring model performance.
this work, we focused primarily on a reasonable model selection
B.3
Generation Parameters and Prompts
B.3.1
Image-Caption Preference Dataset
Caption Generation Setting We employ three state-of-the-art VLMs Qwen2VL-7B, and InternVL-2.5-8B-
generate captions for the given images. For all mod- els, we use the prompt: Provide a factual description highlighting important details in this picture. The generation temperature parameter for LHRS-Bot-Now was set to 0.7, while for Qwen2VL-7B and Intern VL-2255-B, we maintain their standard generation configuration parameters.
Caption Judgment Setting For the selection of captions in our preference pairwise data, we employ GPT-4o (GPT-40-0024-05-13). The complete prompt used for this judgment provided in Table 19
Data Samples We provide several representative samples from our image-caption preference dataset in Figure to illustrate the quality of the collected data.
B.3.2 Vision Instruction Preference Dataset
Judgment Setting We provide the prompt in Table 20 for using Qwen2VL-72B as judger to select the better answer for the input question.
We manually design RS specific questions actoss 12 cate- gories: basic visual recognition, spatial analysis, environmental assessment, urban analysis, agricul- tural analysis, disaster assessment, geological features, infrastructure analysis, temporal understand- ing, advanced reasoning, quantitative assessment, and color spectral analysis. These domain-specific questions presented in Table 21 and Table 22
RS Specific Category and Question
We utilized LHRRS-Bt-- and Qwen2VIL-7B to answer the RS-specific questions. The prompt template employed for this question-answerte task is presented in Table 23
Generation Prompt

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Table 7: Human validation of preference judgments. 'Model- Agreement' measures the model's is accuracy against human judges. 'Inter- Annotttoo Agreement' measures the consistency among human judges on shared subset samples.
Model-Human Agreement
Inter-Annotator Agreement
Accuracy @ ICPD
Accuracy @ VIPD
Human Consistency @ ICPD Human Consistency @ VIPD
90.06% (1351/1500) 96.80% (1452/1500)
88% (44/50)
96% (48/50)
We employ Qwen2.5-13B to rephrase the manually designed questions to enhance question diversity. The prompt used for this rephrasing process is presented in Table [4
Rephrase Prompt
Data Samples We provide serveal samples from our vision instruction preference dataset in Figure [T
B.4 Further Human Validation of Preference Data
While powerful, using general-purpose models like GPT-4o and Qwen2VL-72B for preference judgments warrants careful validation of their accuracy. We chose these models over existing domain-specific VLMs (which are typically smaller, 13B parameters) because they demonstrate superior instruction-following capabilities for the nuanced task of judging responses. For instance, our preliminary human validation showed that GPT-4o achieved 92.6% agreement, significantly outperforming domain-specific model like sure to remote sensing data, the diverse training of GPT-4o and Qwen2VL-72B provides the robust, context-dependent reasoning required for this task.
(82.8%). Despite limited direct expo-
To further validate the accuracy of our preference dataset, we conducted a dedicated human annotation experiment. We selected 3,000 samples of preference data, distinct from those in main manuscript validation, comprising 1,500 from the image-caption preference dataset (ICPD) and 1,500 from the vision-instruction preference dataset (VIPD). Three human annotators were tasked with binary judgment (Yes/No) on whether the chosen positive sample was qualitatively better than the negative sample. Additionally, to assess the reliability of our human evaluation, all three annotators evaluated shared set of 100 common samples (50 from ICPD, 50 from VIPD) to establish inter-annotator agreement.
The results, summarized in Table 7 show that the judgments from our models align well with human preferences. The higher consistency and model alignment VIPD tasks suggest that Instruction-based responses are easier to evaluate than the more subjective, lengthy image captions, where human preferences can naturally vary. While this curation strategy proves effective, acknowledge it is not perfect. Future work could incorporate more complex methods, such as Retrieval-Aune Auggeettd Generation (RAG) with human-annotated data or self-reflection mechanisms, to further refine judgment quality.
C
Additional Result
C.1 Score Model Training
Directly using the output from the final value head as a score creates challenges in defining the range, which affects not only the relative importance of data but also reward calculation in RL training (notably, algorithms like GRPO typically require a reward range of 0-1). To address this issue, we attempt to resolve it at the source by adding normalizing functions after the value head output.
We experiment with sigmoid, tanh, and softplus functions (though softplus only ensures positive scores). The training loss is presented in Figure []] Despite trying different initializations for the final value head, we observe that using tanh and sigmoid as normalization functions led to easy convergence during training (which we interpret as the scores lying in the saturated region of these functions). Furthermore, since we observe that softplus performed worse than using no normalization at all (labeled as "raw" in Figure 5) we opt to use the latter setting during the score model training.
5

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Loss w. Different Normalization Functions
Softplus
1.4
0.72
Sigmold
1.2
Raw
0.70
1.0
0.68
Loss
0.8
0.86
0.64
0.62
0.60
500
1000
1500
2000
50
100
150
200
250
Step
Step
Figure 5: Training loss of score model with different normalization functions
Training Loss
LHRS-Bench BoN Result
1.4
JointTrain
QwenZVL.778 BBL--BB-
1.2
JointTrain
73
Q
1.0
Accuracy (%)
72
Loss
2K
8K
10K
12K
5
10
15
Step
Candidate samples
Figure 6: Comparison of scoring models with different sizes on training loss and performance as BoN selector for LHRS-Bench
Table 8:
Ablation analysis on the effectiveness of scoring model size for distinguishing between good and bad text sample on the hold-out preference set. ICPD = Caage-Caption Preference Dataset, VIPD Vision Instruction Preference Dataset
Accuracy @ ICPD
Accuracy VIPD
Accuracy
Jointly Training 7B
83.13
81.07
82,09
Jointly Training w. 2B
71.53
79.63
75,58
C.2 Ablation Study
C.2.1 Influence of Model Size
We analyze the impact of initialized model size on the effectiveness of our scoring model. Considering the computational cost for full- full- palammrrrettr tuning models larger than 7B, we evaluate the smaller Qwen2VL-2B model as comparison to Qwen2VL-7B. For simplicity, we compare the results using joint training strategy introduced in Section 4.4 ensure fair comparison, we use the same model family (Qwen2VL.) and identical optimization steps (i.e., same batch size). We sweep through wide range of learning rates from 10-7 to 1 10-1 for Qwen2VI--BB, and find that 8 x 10-5 yields the best results (according to the training loss), compared to 10 for Qwen2VL-7B.

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VG-DIOR
LHRS-Bench
ScoreRS
Init.
Init.
Accuracy (%)
72
71
57
56
Candidate samples
Candidate samples
Figure 7: BoN comparison different score model with different initialziction base model. ScoreRS initialized with Qwen2VL-7B, RS init. is initialized with Qwen2VL-FT from Table and RS init. i is initialized with Qwen2VL-FT (ScoreRS-P30SS) from Table 3
Table Comparison of different initialization strategies for the scoring model. We initialize scoring models with different base models, use them to filter the VHM instruction dataset with 60% selection (coreRS-P30) from Table 3 with the filtered data. ScoreRS is
ratio, and fine-tune Qwen2VL-FP initialized with Qwen2VL-7B. RS init. is initialized with Qwen2VL-FT from Table and RS init. initialized with Qwen2VL-FF
SScoreRS-P3OSol from Table 3
AID METEBAI
AWP
SUILWHU
WHL--SSI
HR-Comp
HR-Prs
LK-Corm
Lit-bes
LI--UU
Ang.
VG-DXK
LHUKS deach
1320
1430
31207
11:3
12.21
13-27
1238
$330
5633
2730
31384
3531
16:33
Owwen.VV VT..FTTB
35.38
86.11
We compare the two variants' training loss, accuracy the held-out preference set described in Section 4.4 and their performance as BoN selectors. From the training loss and BoN results shown in Figure 6 observe that the smaller model converges higher loss compared to the larger model. On LHRS-Bench as selector, the smaller selector performs inferior to the larger selector candidate samples, indicating that smaller models struggle
and tends to saturate when given to identify the best candidate among different samples. On the hold-out preference evaluation set presented in Table we see that the 2B model performs approximately 7% worse compared the 7B model, further demonstrating its reduced effectiveness in distinguishing good responses from bad compared to the larger model.
These results confirm that larger scoring models indeed provide better performance, which aligns with related work [17]. We anticipate that future work could develop even larger, more advanced scoring models to bring additional benefits to the RS community.
C.2.2 Influence of Model Initialization
Next, we tackle a critical question: whether initializing the scoring model with RS-specific VLM instead of general VLM (QwenVL.2-7B in our case) provides benefits. We use our fine-tuned Qwen2VL-FT from Table (i.e., the model fine-tuned with all VHM datasets) to initialize the scoring model and conduct the three-stage training with the same preference data. this setting, we maintain the original base architecture (Qwen2VL) and training recipe to ensure fair comparison. For evaluation, we use the different scoring models to filter the VHM vision instruction dataset with fixed selection ratio of 60%, and use the filtered instruction dataset to fine-tune Qwen2VL-FT (ScoreRS- -P33) from Table The fine-tune setting is the same as Section D.4 We also evaluate them BoN selectors VG-DIOR and LHRS-Bench with our Qwen2VL-7B RS- The BoN evalution setting is the same as Rection[..4
The results are presented in Table and Figure 7 From these results, we observe that initializing the scoring model with an RS-specific VL.M yields inferior performance in both data filtering quality and BoN selection compared to using the general Qwen2VL as the base model. We hypothesize
7

---

Table 10: Comparison different finetuned CLIP models on classification tasks
EandAt
Alpes
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Auges
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4611
6999
11.11
7144
63.0
63.0
1630
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1338
483
105
6399
9129
139
15.0
5337
3773
Table 11: Performance of different data filtering methods at various saving ratios. Lower saving ratios indicate more extreme data filtering.
Saving Ratio
Method
Classification
Retrieval
ScoreRS
70.97
27.11
30%
CLIP-Score
69.98
26.36
ScoreRS
68.44
27.01
20%
CLIP-Score
67.70
26.69
ScoreRS
63.19
26.16
10%
CLIP-Score
62.89
26.05
that this occurs because Qwen2VL-FT is fine-tuned with the complete VHM dataset, which likely contains low-quality vision-language data that biases the base model toward domain that difficult to optimize for high-quality sample selection.
To test this hypothesis, initialize the scoring model with an RS-specific VLM that is fine-tuned with ScoreRS- data sitta-ppcifiiclly.. Qwen2VL-FT SScoreRS-P3OS66 from Table With this modification, the new scoring model (RS init.)) performs better both data filtering and BoN selector, which supports our hypothesis.
Our analysis indicates that if we can ensure the RS-specific VLM is of high quality (though "high quality" may be difficult to precisely define, it can be approximated through data that has undergone cleaning through qualified pipeline), specialized initialization of the scoring model will yield better performance.
C.3
CLIP Training
C.3.1
Evaluation on Skyscript Dataset
extend our investigation by applying ScoreRS to select data from the larger-scale RS image- caption dataset Skyscript (62]. Except for the dataset used, all experimental details remain identical to our RemoteCLIP lne-tuning setup.
The results, presented in Table TO further demonstrate that filtering with our ScoreRS yields superior performance compared to both using the complete dataset and applying CLIP-score filtering. We ob- serve that directly fine-tuning CLIP with the entire Skyscript dataset actually resulted in performance inferior to the original CLIP model. We attribute this the rule-based caption construction method used in Skyscript, which introduces significant redundancy and thus necessitates more careful data selection for effective fine-tuning.
C.3.2 Evaluation Extreme Data Filtering Scenarios
We investigate the impact of extreme data filtering by evaluating performance at various data saving ratios, as shown in Table The results demonstrate that data quantity remains crucial for maintaining high performance, particularly for classification tasks. Extreme filtering ratios significantly degrade classification accuracy, likely because datasets with numerous classes require sufficient number of samples per class learn effectively. contrast, retrieval tasks appear more resilient to data reduction. This suggests that even small, high-quality subset of data can effectively capture essential text-to-image alignment concepts required for retrieval, thus maintaining comparable performance under extreme pruning.

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Table 12: Performance comparison of different filtering methods on the curated RemoteCLIP dataset (30% saving ratio). The classification benchmark includes NWPU. AID, METERML, WHU-RS19, SIRI-WHU, fMoW, and EUTOSAT. The retrieval benchmark uses UCM and RSICD.
Method
Classification Avg @ Retrieval Avg @
ScoreRS
70.97
27.11
CLIP-Score
69. ,999
26.36
SigCLIP-2
68.24
26.47
CLIP-LAION-RS
70.59
26.41
Table 13: Comparison between ScoreRS-basee GPRO and DPO RL training. DPO/SK represents training with the same data scale as GPRO training, while DPO/26K represents training with the complete preference dataset for DPO training
VG-DIOR
LHRS-Bench
58.97
66.45
Qwen2VL-77-RS-DDOOO2-
59.71
67.19
Qwen2VL-7B-RSS-Zer
59.64
67.21
C.3.3 Comparison with State-of-the-Art Filtering Methods
To further evaluate the effectivness of our ScoreRS, we conduct analysis using robust, publicly available models as baselines. We noted that CLIP-LAION-RS 62). which was fine-tuned on a remote sensing (RS) subset of the well-curated LAION-2B dataset, outperforms standard CLIP- filtered versions. We, therefore, curated the RemoteCLIP dataset using scores from both SigCLIP-2 (L/16-256) and CLIP-LAION-RS (L/11)), comparing their filtering performance against our ScoreRS method. For this experiment, we used a fixed data saving ratio of 30%.
The results are presented in Table I2 Although SigCLIP-2 generally exhibits strong performance various tasks compared to the original CLIP models, factors such as data distribution and training recipes may lead to different outcomes in the specialized remote sensing domain. The SigCLIP- 2 filtered version shows inferior performance on classification tasks, while the CLIP-LAION-RS filtered version shows marked improvement over the standard CLIP-Score. Notably, our ScoreRS filter demonstrates the best performance among all baselines, achieving the highest scores in both classification and retrieval.
From these experiments, it is clear that ScoreRS is the first scoring model in the RS community capable of effectively evaluating diverse types of vision-language data. Additionally, thanks to the strong capabilities its Qwen2VL backbone, it can be extended to evaluate complex multi-image vision-language data, such as change captioning tasks.
C.4 RL Comparison
C.4.1 Compare with DPO
We compare the effectiveness of using ScoreRS as reward function for open-ended questions with GPRO against Direct Preference Optimization (7) Specifically, we use the 26K vision instruction preference pairs (excluding the RS-specific preference data) constructed Section (.2tiion3.2.2 for DPO training. Starting with our fine-tuned Qwen2VL.-7B-RS model (Table 55, we conduct full-parameter DPO training using LLaMA-Factory and compare the results with Qwen2VL-7B- Zero (Table 5p. For fair comparison,
train two DPO variants: one using 8K preference pairs (comprising 4K open-ended questions and 4K closed-ended questions), and another using the full 26K preference dataset. We evaluate the resulting models VG-DIOR and LHRS-Bende evaluation datasets.
The results are presented in Table 13 We observe that with equivalent data scale, DPO performs inferior to GPRO training, while increasing the data scale for DPO yielded performance on par with or slightly better than GPRO training. This observation aligns with the established conclusion that RL training is typically more data-efficient than SFT [5] (here, we consider DPO generalized form of SFT). In our case, the data efficiency advantage is approximately 3x (8K vs. 26K).

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Reward
Response Length
Eval. @ LHRS-Bench
0.70
0.8
0.65
Total Reward
200
Accuracy
Length
200
0.55
0.6
0.50
169
100
0.40
20
40
80
100
20
60
100
20
40
80
100
Step
Step
Step
Figure 8: Comparison with GPT-4o reward function for open-ended questions
Table 14: Comparison between Qwen2VL-7B-RS and its PPO training variant
BEVOA
Unvrading
Kamalege
LIBIS
QpenOVE-HH-B QWAI2VL-78 RS-PPO
X5.0
2442
34.5
W1.6
$4.59
11.30
75.89
91.36
WL3
38033
96.45
26.4
67J8
96.76
630
9065
70.29
92:00
$1.7
35.00
72.30
15332
9685
92.99
94.73
57.10
66:4
C.4.2 Compare with GPT-4o as Reward Model
Since preference dataset is largely built using GPT-4o as judge (excluding closed-ended questions) based on our scoring system, a natural question arises: would using the same scoring system with GPT-4o the reward model for calculating rewards for open-ended questions be more effective? To investigate this, we utilize GPT-4o for reward calculation based on our scoring system and normalized the total reward (e.g., average reward 5) as the final reward for open-ended questions. We start with the Qwen2VL-7B-RP experimental settings as described in Section 4.2 We compare these results with those obtained using our ScoreRS(i.e., compared with Qwen2 ----22V------------ RS--ooo....
model from Table | and use the same training dataset and
We plot the total reward, response length, and evaluation results LHRS-Bennhs during the training process in Figure [8] The results show that despite our dataset being largely built with GPT-4o, using GPT-4o the open-ended reward model performed inferior using ScoreRS. We suspect two main reasons for this outcome. First, since our ScoreRS is fine-tuned with large-scale RS-specific preference data, although the dataset is built using GPT-4o judgments, it acquires substantial domain- specific knowledge (or simply put, developed bias toward this specific domain) that helps better distinguish between good and better responses. Second, the closed-ended preference dataset enables specific capabilities in ScoreRS. Moreover, we should note that using GPT-4o as reward model during the training process is significantly more costly than using ScoreRS (here, cost refers to money, with the single training run costing approximately $360).
C.5 BoN Selection
We further evaluate our ScoreRS on more advanced VLMs, including Intern VL.3-8B and Qwen2.5VL-7B [56]. To validate the effectiveness of our approach, we compare it with majority voting (i.e., selecting the most common candidate as the final answer). The generation sampling parameters are the same those described in Section 4.3 The results are presented in Figure
Our findings clearly demonstrate that even with ScoreRS can boost performance mechanism. Notably, for the RS vision grounding task, since the bounding boxes generated vary across different candidate samples, majority voting often fails to improve model performance, while our ScoreRS consistently delivers improvements on this task.
advanced (i.e., more optimized) models, our BoN selector and outperform the majority voting selection
C.6 PPO Training
We explored using our ScoreRS directly as reward model for RL through proximal policy opti- mization (PPO) [50]. We employ our finetuned Qwen2VI.-7B-RS as the policy model, while the critic model is initialized with the same Owen2VL-77-BR 7B-RS architecture but with its language head
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VG-DIOR
LHRS-Bench
40.0
76
Acouracy (%)
36.0
74
32.5
72
70
27.5
25.0
22.5
10
15
10
16
Candidate samples
Candidate samples
InternVL3--BB ScoreRS
QwVL2.5VL-7B w. ScoreRS
InternVL3-8B w. Major
Qwen2.5VL-78 w. Major
Figure 9: BoN comparison advanced models with ScoreRS selector compared majority
voting
replaced by learnable linear layer. We utilize the same dataset described in Section 4.2 for this training process. LoRA is applied to all linear layers in the policy model with rank and parameters both set to 64. For text generation, we configure sampling temperature of 0.95 and top-p of 0.9, with maximum new token generation limited 512 tokens. The implementation uses rollout batch size of 16, with PPO buffer size of 8 and PPO epochs. We initialize the KL penalty coefficient at 2 and gradually increase it to 6 throughout the training process. The À parameter for Generalized Advantage Estimation (GAE) [9 set to 0.95. For optimization, we use a learning rate x 10-5 with 0.1 warmup ratio and cosine learning rate decay schedule.
The results presented in Table reveal that the model after PPO training performs inferiorly to its base variant across almost all evaluation datasets. We suspect this underperformance stems from suboptimal hyperparameter selection and reward calculation. Since we directly use our ScoreRS as reward model without any normalization hacking []. Moreover, given the considerable number parameters associated with the PPO algorithm, expect that developing comprehensive reward calculation strategies and conducting thorough hyperparameter searches remain important directions for future work.
reference-basee approach, this may be caused by reward
D Experimental Setting
D.1 Hardware and Framework
All our experiments are conducted on 2 nodes, each equipped with x A100-80G GPUs. For training the scoring model, develope customized training framework based on the OLMo training framework For fine-tuning the large VLMs, we utilized the LLaMA-Factory training framework For RL traiinng we customized our framework based on the VeRL training framework
We implement our scoring model an API call for reward caleulation during the RL training process. During our RL training, we observe that the bottleneck is the reward calculation process when using a single scoring model to evaluate rollouts from different actors. maximize training efficiency, we deploy multiple scoring models on different GPUs in different node (specifically, 8 scoring models our case), and each actor calls a specific scoring model according local rank. This approach significantly increased our RL training efficiency.

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Table 15: Hyperparameter for training our ScoreRS
Stage
Stage 2
Stage 3
Batch Size
64
16
Weight Decay
0
0.1
Learning Rate
2 x 10-5
x 10-6
WarmUp Iter
500
Epoch
1
Gradient Accumulation
2
D.2 ScoreRS Training
We implement a three-stage training approach for our ScoreRSmodel. Throughout all stages, we employ the AdamW optimizer with cosine learning rate decay and set (8i, 82) to (0.9, 0.95). For computational efficiency, we implement ZeRO-2 optimization with b precision across all training stages. Additional hyperparameters are detailed Table T5
D.3 CLIP Finetuning
We utilize CLIP-ViT-L/14 from Hugging Face as our base model. Across all fine-tuning experiments, we employ ZeRO-2 optimization with bfloat 16 precision and the AdamW optimizer.
For fine-tuning on RemoteCLIP, we configure batch size of 1,024, learning rate of 1 x 10-5, weight decay 1.0, and warmup iterations of 200. The model fine-tuned for 5 epochs using cosine learning rate decay.
For fine-tuning on Skyscript, we use a larger batch size of 2,048, learning rate of 10-5, weight decay of 0.01, and warmup iterations of 2,000. The model is fine-tuned for 20 epochs with fixed learning rate schedule, reducing the rate by a factor of 0.316 at 80% and 90% of the training process.
D.3.1 Evaluation Setting
We evaluate the fine-tuned CLIP model on both RS classification and RS image-text retrieval tasks. For the classification tasks, we employ text prompts in the format of "a satellite photo of (class name]" and "a satellite image of (elass name/" to perform the classification.
Evaluation Metric: For the classification task, we report top-1 and top-5 accuracies. For the retrieval task, we evaluate both image-to-text and rext-to-image performance using top-1 and top-5 recall metrics.
Evaluation Dataset: (1) NWPU (NWPU-RESISC45) contains 31,500 RGB images devided into 45 scene classes, each class containing 700 images. We use the entire dataset for evaluation. (2) EuroSAT [20] is based on Sentinel-2 satellite imagtes consisting out of 10 classes with total 27,000 labeled images. We use the entire dataset for evaluation. (3) fMo W [10] contains 1, 1,047,691 images convering 207 country and the total images are devided into 63 categories. We use the test split of fMoW-RGB for evaluation (16,948 samples). (4) AID 67 contains 10,000 images from 30 different categories. We use the entire dataset for evaluation. (5) SIRI-WHU [37 is a classfication dataset that contains 2,400 images from 12 classes, with each class has 200 images. We use the entire dataset for evaluation. (6) WHU-RS19 [13] contains 19 classes, with each class has about 50 samples. We use entire dataset for evaluation. (7) UCM (UCM-Caption) 34 contains 2, 100 images, with each image has 5 different captions. We use the same retrieval evaluation recipe as Skyseript (62). (8) RSICD (34] contains 10,921 images, with each image has different captions.
We use the same retrieval evaluation recipe as Skyscript 34
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Figure 11: Representative examples from our vision instruction preference dataset. Green represents reasonably good expression, while red represents low-quality expression
D.4 Large VLMs Finetuning
We select Qwen2VL-77 7BB-Instrcct as our base model and utilize the RS image-caption and vision instruction data from VHM as our finetuning dataset. Due to special token format differences between the VHM dataset and Qwen2VL-7B, we implement rule-based conversion methods to align the special tokens with Qwen2VL-7B requirements.
Our fine-tuning approach consists of two sequential stages: 1. In the first stage, we finetune the model using image-caption data while only unfreezing the vision-language connector. 2. In the second stage, we finetune using vision instruction data, applying LoRA adaptation to the base L.LM while maintaining the unfrozen vision- vision-language connector.
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For both stages, we employ the AdamW optimizer with ZeRO-2 optimization strategy, bfloat precision, a maximum context length of 8,192, and cosine learning rate scheduling, Each stage is trained for epoch.
Stage-specifie hyperparameters
follows:
First stage: learning rate of 8 10-5, batch size of 64, weight decay of 0.01, warmup ratio of 0.1, and maximum image resolution of 768.
Second stage: learning rate of 2 x 10-4, batch size of 64, weight decay of 0.01, warmup ratio of 0.03, and increased maximum image resolution of 1,024.
D.4.1 Evaluation Setting
We adhere to the evaluation dataset split established the VHM and utilize their task-specific prompts for each evaluation task. To ensure fair comparison, we augment each prompt with the appropriate task identifier when evaluating models that support task identifier. For response generation across all models and tasks, we maintain consistent hyperparameters with temperature of 1.0 and top-p of 1.0.
Evaluation Dataset: (1) Classification: Brief introductions to each classification dataset can be found in Section D.3.T (2) RSVQA(33: RSVQA is a RS vision question answering dataset available in high resolution (RSVQA-HR) and low resolution (RSVQA-LR) versions. RSVQA-HE contains four question types: comparison (comparing object quantities), presence (determining images contain specific objects), counting (quantifying objects), and area (measuring object areas).
RSVQA-LR includes an additional rural/urban type to classify image settings. Following established practices in existing RS-specific VLMs such as VHM[44),, SkysenseGPT [36). and GeoChat (27), we excluded questions related to counting and area measurement for evaluation. (3) Grounding:
We used VG-DIOR (74] for vision grounding tasks. VG-DIOR is RS vision grounding dataset built from RSVG [74] on the DIOR RS detection dataset (28). Each sample contains an object description that requires the model to predict object coordinates. We calculated the IoU between predicted and ground truth coordinates, considering predictions with loU greater than 0.5 as correct. (4) General Knowledge: We used LHRS-Bench (39 to holistically evaluate VLMs across different domains. LHRS- Bench is a multiple-choice question answering dataset covering 11 evaluation dimensions (from basic recognition to complex reasoning). For evaluation,
used the following prompt template: Question. Please answer the above question with the given choice (just answer with choice index): Choices. We extracted choice indices (e.g., A, B. C, D) using regular expressions and only considered responses containing the exact choice index as correct. Responses that included the content of the choices were not counted as correct. We did not implement the circular evaluation protocol introduced in [39].
D.5 GRPO Training
We implement the GRPO algorithm with our ScoreRS model for rewarding open-ended question responses. We select GRPO due to its simplicity and computational efficiency.
For open-ended vision question answering tasks, employ binary accuracy reward: if the prediction matched the ground truth, and 0 otherwise. For bounding box prediction tasks, we implement graduated reward system based IoU: 0.5 for loU >> 0.5, 0.6 for IoU >> 0.6, 0.7 for IoU > 0.7, and 1.0 for loU >> 0.8, with O reward otherwise. The application of ScoreRS for close-ended questions has been previously described in Section 3.3] and the hyperparameter in Equation 3 is set to 0.2. We also weight the format reward at 0.3 following the practice established in RI-VL 76).
We conduct full parameters training during the GPRO RL process. During the generation phase of GRPO, we configure the sampling temperature to 0.9, and top-p to 0.99. The maximum generated tokens limited to 4096. We set the KL penalty coefficient to 0.01 and use batch size of 16, with each sample generating 5 candidate answers for reward calculation.
We optimize the model using AdamW with cosine learning rate schedule and base learning rate of 1 x 10-6 6 To improve computational efficiency, we utilized 6 precision, gradient accumulation with factor of 2, and maximum image resolution of 1024 pixels. Memory consumption is reduced
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by implementing Flash Attention in conjunction with FSDP strategy 80 conducted for 2 episodes.
All experiments are
For the supervised finetuning of Qwen2V1-77-RRS with our manually collected reasoning data, we employ identical hyperparameter settings described in Section D.4 with the LoRA rank set 128.
Evaluation Setting The evaluation metrics, datasets, and framework for assessing the trained reasoning model remained consistent with those detailed in Section D.4 The key distinction is the implementation of an answer extraction process from the reasoning model's output for evaluation purpose. Importantly. if this parsing process failed to extract valid answer from reasoning model, we classify the response as incorrect.
E
Qualitative Example
E.1 Ranked Data Comparison
In order provide qualitative comparison of samples receiving higher scores from different scoring models, we plotted the highest and lowest ranked samples from RemoteCLIP, VHM image-caption, and VHM vision instruction datasets Figure
From these results, we observe that for RemoteCLIP image descriptions, ScoreRS prefers more detailed descriptions while assigning lower scores to samples containing incorrect qualitative de- scriptions or claims that cannot be verified from the image. For the VHM image-caption dataset, ScoreRS can precisely identify accurate descriptions while filtering out incorrect or hallucinated content more effectively than LLIP-score.Finalle regarding the VHM vision instruction dataset, clearly see that ScoreRS favors samples that demonstrate high-level reasoning and relevance to specific applications, while effectively filtering out incorrect answers.
E,2 Qwen2VL.-7B-RS-R Inference Example
We provide representative conversation examples generated by our finetuned model, Qwen2VIL-7B
RS-RI, in Figure
F
Discussion nand Limitation
F1 CLIP-Score or ScoreRS?
The results in Table [ Table and Table I0 indicate that the performance improvement achieved through data filtering with ScoreRS may be somewhat marginal compared to CLIP-score (approxi- mately 1 1% improvement to acknowledge that the RemoteCLIP and Skyscript datasets typically contain only short, brief de- scriptions (e.g.. A ship in the middle the picture), which aligns with the type of text captions CLIP is trained on [48 669. Furthermore, during evaluation on classification tasks, we typically employ simple text templates such as A photo of (class name/ as the representative embedding for class matching. The descriptions used in retrieval evaluation tests are similarly short and concise. In con- trast, the preference data used to train our ScoreRS typically consists longer, more informative text (Figure 10)), which may explain why ScoreRS does not show substantial advantages when qualifying these particular datasets. Nevertheless, even in these scenarios, our ScoreRS outperforms CLIP-score, demonstrating the significant potential of using VLMSs scoring models for data qualification.
RemoteCLIPP data, while 4% Skyscript). However, it is important
We should acknowledge that for applications and datasets similar to RemoteCLIP or Skyscript, using CLIP-score is a reasonable approach. However, for qualifying more complex vision-language data and for advanced applications such as BoN selection and reinforcement learning, ScoreRS offers superior performance, as evidenced by Table Figure 4 and Table
F.2 Reasoning for Reference Based Reward Calculation
In Section 3.3
we introduce the reference-based reward mechanism using our ScoreRS for utilizing open-ended questions. Before arriving at this approach, we explored multiple strategies. The following describes unsuccessful attempts and our reasoning:
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Direct Reward We initially attempted to directly use the output of ScoreRS for rewarding. However, since the output of ScoreRS can be significantly larger or smaller than the 0-1 range, and we do not know the possible range of the ScoreRS output (therefore, could not normalize them), the training proved very unstable. Additionally, the rule-based reward for close-ended questions was greatly overshadowed. Although we acknowledge the potential challenges and attempt to mitigate them during the training process of our ScoreRS, the results presented in Appendix C.I demonstrate that using raw scores for training provides the best training stability.
Function Normalization Reward We then try normalizing the ScoreRS output with sigmoid or other similar normalization functions (tanh). This approach was problematic because the range of our ScoreRS output likely falls in the convergence part of these functions (for example, in the > 10 or < -10 regions of the sigmoid function). Although we find ways to sketch the domain of the function, learning remains too slow and does not yield satisfying results.
With these unsuccessful attempts behind us, we opt for the reference-basee reward calculation. Although the requirement for reference datasets may reduce the possible data size for RL training, we conclude that data volume is not the critical part of RL training- better answer sampling and reward strategies are far more important. Of course, we do not consider this the optimal solution, but rather our current finding, and we hope future work will explore more effective approaches.
E.3 RS Vision-Language Data Quality
In this work, we have developed a framework to evaluate the quality of RS dision-languall data and demonstrated the effectiveness of parameter-wise learned scoring models for RS vision- saiiunl data selection. Our investigation reveals that current RS vision-language datasets fall considerably short of optimal quality, underscoring the need for more rigorous curation efforts in this domain- -ppeifc context.
As the machine learning community increasingly validates the "less is more" principle 25
the RS community should prioritize data quality improvement through systematic quality control mechanisms or scoring model implementations. While our work highlights the suboptimal quality of existing RS vision- language data using our ScoreRS, we acknowledge that ScoreRS is merely an initial attempt to qualify such data. Future research should investigate whether using separate scoring models for each quality dimension introduced in Appendix Ajoould bring additional benefits. Further exploration should also address various dimensions including data difficulty levels, category distributions, and optimal data combinations to fully harness the potential of RS-specialized VLMs.
F.4 Expectation for Building RS-Specific VLMs
Throughout our investigation and evaluation, we gained significant insights regarding the misalign- ment between current data characteristics and the capabilities expected from advanced VLMs.
Modern large-scale VLMs excel not only in perception tasks but also in reasoning and planning for complex problem-solving [8I]. For the RS community, these models should ideally support sophisticated applications such disaster analysis, urban planning, and transportation system assessment. Our analysis suggests that current Rpecciice VLMs, while performing admirably on basic perception tasks, fall short on more complex challenges compared to general VLMs (Table 4
Since basic perception tasks can effectively addressed with more lightweight models (for example, accuracy AID can achieve over 95% with the much smaller ViT-B architecture (60), the devel- opment of large RS- specifii VLMs could, in our view, put more focus on expanding beyond these foundational capabilities toward building agent-like assistants that interact with RS experts to conduct geological information analysis. This requires equipping models with more advanced capabilities such planning, task decomposition, and reflection. Therefore, while continuing to value and build upon the important work in perception tasks, we suggest that future RS-specific VLM development could benefit from increased emphasis these more complex reasoning capabilities to fully leverage the potential of LLMs in RS applications.
We advocate for the development of VLMs that transcend basic perception tasks and deliver advanced analytical capabilities specific to RS applications. Furthermore, we hypothesize that exposure to more challenging, appiication-orientedd RS analysis tasks could paradoxically enhance these models'
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fundamental perception capabilities 722 The creation of high-quality, application-specific datasets represents a critical direction for future work in this domain.
17

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Figure 13: Representative conversation examples demonstrating the capabilities of our finetuned Qwen2VL-7B-RS-P model
18

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Table 16: Scoring system for RS image-caption
Aspect
Score
Definition for Descriptions/Caption
(Poor)
The description is largely irrelevant to the image, contains significant
hallucinations (objects/features clearly not present), or fundamentally
1. Relevance
misrepresents the scene.
2 (Weak)
The description has notable inaccuracies, some hallucinated minor ele- ments, or only vaguely relates to the primary content of the image. The description is generally relevant and mostly accurate, but may con- tain minor inaccuracies, slight misinterpretations, or omit verification of some mentioned details.
3 (Fair)
4 (Good)
The description is accurate, clearly relevant, and faithfully represents the main visual content with no significant hallucinations or misinterpreta- tions. Minor, non-critical details might be simplified. The description perfectly accurate, highly relevant, and precisely reflects all key visual elements and their relationships in the image without any ambiguity or hallucination.
5 (Excellent)
i (Poor)
The description is extremely vague (e.g., "an image of the ground." "some features") and provides almost no useful detail about the RS image content.
2. Specificity& & Detail
2 (Weak)
The description is mostly general, lacking specific details about objects, patterns, spatial arrangements that clearly visible.
3 (Fair)
The description provides some specific details but remains general in many aspects. Key distinguishing features might be mentioned but not elaborated upon.
4 (Good)
The description offers a good level of specific detail about important objects, their characteristics (if disceriibee). and their general layout. The description is rich in specific, fine- aineggriind details about objects, textures, shapes, counts (where appropriate), and their precise spatial relationships.
5 (Excellent)
1 (Poor)
The description misses almost all salient and important features/regions
in the RS image.
3. Completeness
2 (Weak)
The description omits several significant features or covers only a small, mon-representative part of the image.
3 (Fair)
The description covers some of the main features but misses other notable
ones or lacks comprehensive coverage of the overall scene.
4 (Good)
The description covers most of the salient features and important regions of the image adequately.
5 (Excellent)
The description provides a comprehensive account of all major salient features, land cover types, and significant patterns visible across the entire image.
1 (Poor)
The description is largely incomprehensible, grammatically incorrect, or uses language so poorly that its meaning is lost.
4. Clarity& & Fluency
2 (Weak)
The description is difficult to understand due to significant grammatical errors, awkward phrasing, or unclear language.
3 (Fair)
The description is mostly understandable but contains some grammatical errors, awkward phrasing. or minor ambiguitics.
4 (Good)
The description is clear, grammatically correct, and well-phrased, mak- ing it easy to understand.
5 (Excellent)
The description is exceptionally clear, concise, grammatically flawless, and uses precise, fluent language.
1 (Poor)
The description uses no relevant remote sensing terminology or misuses
terms completely. Lacks any understanding of RS RS-specific concepts.
5. Semantic Richness
2 (Weak)
The description uses very generic terms (e.g., "green areas," " "buildings") with minimal or incorrect RS-specific vocabulary.
3 (Fair)
The description uses some basic and appropriate RS terminology (e.g.. "urban area," "farmland") but lacks depth or precision in describing
RS-specific features.
4 (Good)
The description correctly employs relevant RS terminology to describe features, land cover, or patterns (e.g., "center-pivot irrigation," "industrial "industria zone").
5 (Excellent)
The description expertly uses precise and advanced RS terminology. accurately identifying and describing complex features, phenomena, sensor characteristics relevant to the image.

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Table 17: Scoring system for RS instructions samples (Part 1)
Aspect
Score
Definition for Instruction/QA Pairs
(Poor)
The answer is completely irrelevant to the question, is based entirely hallucinated information not in the image, confidently answers an unanswerable question incorrectly.
1. Relevance
2 (Weak)
The answer poorly addresses the question, contains significant inac- curacies based on the image, or makes large, unfounded assumptions. Wrongly asserts unanswerable question answerable.
3 (Fair)
The answer attempts to address the question and is mostly based on the image, but may have minor inaccuracies, misinterpretations, or if the question is unanswerable. it may fail to state this clearly or attempt a guess.
4 4Goodd
The answer accurately and directly addresses the question using information verifiable in the image. If the question is unanswerable from the image, it states so clearly.
(Excellent)
The answer perfectly and precisely addresses all aspects of the ques- tion using only visual evidence from the image. If unanswerable, it explicitly and correctly states why based on image content.
(Poor)
The answer is extremely vague (e.g.. "Yes," " "Maybe." "Objects are present") and provides no specific information from the image rele- vant the question.
2. Specificity & Detail
2 (Weak)
The answer is too general and lacks specific details that are visible in the image and pertinent to answering the question effectively.
3 (Fair)
The answer provides some relevant detail but could be more specific or elaborate further based on visible image content and the question's ss needs.
4 (Good)
The answer provides a good level of specific detail directly from the image that sufficiently addresses the question.
(Excellent)
The answer is highly specific and rich in relevant details extracted from the image, providing precise and thorough response to the question.
1 (Poor)
The answer completely fails address the core of the question ignores key components of a multi-part question.
3. Completeness
2 (Weak)
The answer addresses only small part of the question provides a very superficial response, missing obvious follow-up details implied by the question and visible in the image.
3 (Fair)
The answer addresses the main part of the question but may be incomplete. missing some nuances, or not fully utilizing available visual information. If question is partially unanswerable, this might not be fully clarified.
4 4Good)
The answer comprehensively addresses all explicit parts of the ques- tion using available image information. Clearly indicates if parts are unanswerable.
(Excellent)
The answer fully and exhaustively addresses all aspects the ques- tion, including implicit sub-questions where appropriate, based on thorough image interpretation. Clearly delineates what can and can- not be answered.
1 (Poor)
The question is incomprehensible, or the answer is grammatically nonsensical, making the entire QA pair useless.
4. Clarity & Fluency
2 (Weak)
The question is ambiguous, or the answer is poorly phrased with significant grammatical errors, making the QA pair difficult to under- stand or trust.
3 (Fair)
The question is mostly clear, and the answer is generally understand- able but may contain minor grammatical errors, awkward phrasing. or slight ambiguities.
4 (Good)
The question is clear and unambiguous, The answer is well-phrased. grammatically correct, and easy to understand.
5 (EExellent)
The question is exceptionally clear and well-posed. The answer is perfectly fluent, concise, grammatically flawless, and directly responsive.
20

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Table 18: Scoring system for RS instructions samples (Part 2)
Aspect
Score
Definition for Instruction/QA Pairs
(Poor)
Neither the question nor the answer uses any relevant RS terminology, terms severely misused, showing no domain understanding.
5. Semantic Richness
2 (Weak)
The QA pair uses very generic terms. If RS terms are used, they are minimal, overly simplistic for the context, or slightly incorrect.
3 (Fair)
The question or answer uses some basic, appropriate RS terminology. but could be more precise or leverage more foomann-speciin knowl- edge relevant to the image.
4Good)
The question and/or answer correctly employ relevant RS termi- nology that enhances the specificity and technical accuracy of the exchange.
5 (Excellent)
The QA pair expertly uses precise and potentially advanced RS terminology. The question might probe RS-specific insights, and the answer provides them accurately.
Table 19: Prompts for selecting image-caption preference pair
System Message:
You highly precise and analytical image description evaluator. Your task select most accurate caption from given options or provide your own if none are satisfactory. You should approach this task systematically and provide detailed justification while maintaining objectivity.
Prompt:
Please analyze the given image and evaluate these three captions:
Caption [caption
Caption [caption2)
Caption 3: {caption3)
Follow these steps:
1. First. provide your detailed description of the image, focusing
Observable objects their characteristics
Spatial relationships and positioning
Key visual patterns and structures
Colors and textures
Avoid making assumptions about image type or source.
Evaluate each provided caption using the following criteria:
The ceiteria in Table Td
3. Provide your analysis in the following format: [description]
your independent description
[end-description]
[evaluation]
:: [score], [scoor]], [[score]] (ccore)
[score], |. |score] [xcore] [score]
[score]. [end-evaluation]
[selection]
best-id: [id]
best-reason: [reason]
[end-selection]
21

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Table 20: Prompts for selecting vision instruction preference pair
Prompt:
are an expert evaluator specialized determining the quality of answers. Your task
systematically analyze and compare answers to determine which one is better. You must:
1. Always analyze the core requirements of the question first
2. Break down both the given answers into key components
3. Compare them systematically and objectively based the given criteria
4. Provide conclusions a strictly formatted output and
Your evaluation must be based on the following criteria:
The criteria in Table + and Table ++
Now, please evaluate the following answer pair: QUESTION:(question
ANSWER:
[answerl]
[answer2)
[answer3)
Provide your evaluation with following format: [evaluation]
:: [scorr] [scorre].ssccrre_
[score] [scoree) [scoree
[score].[score (.sccorrre[[ccrre]] [scoorer_sscrr]///.. [score
[end-evaluation]
[selection]
best-id: [id]
best-reason: [reason]
[end-selection]
22

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Table 21: Manually designed questions for RS specific applications (Part 1)
Basic Visual Recogni tion
What the dominant land cover type
image?
How many distinct types of land cover can you identify in this image?
What percentage of the image is covered by water bodies?
Are there any clouds present in the image? If yes, approximately what percentage of the image is cloud-covered?
What season does this image appear to be taken in? What visual cues support your answer?
Spatial Analysis
What is the approximate scale of this image? in relation natural features.
Describe the spatial distribution of urban cattyscalkreggiona//oooonnee
What patterns human settlement can you observe? celustrreodiisperccdiieiee
How does the terrain influence the distribution of vegetation?
Can you identify any transportation networks? How do they relate to urban development?
Environmental Assessment
Are there any visible signs of environmental degradation?
Can you identify potential areas soil erosion?
What evidence of water pollution can you observe?
How healthy does the vegetation appear? What indicators are using?
Are there any visible impacts of climate change extreme weather events?
Urban Analysis
What is the predominant urban development pattem??
Can you identify diffcrent types of urban land use fosiidetialccommeecca//nnntsiiai
How well-connected is the transportation infrastructure?
Are there clear boundaries between urban and rural areas?
Can you identify any informal settlements or rapid urbanization patterns?
Agricu curiculio tural Analysis
What types of agricultural practices are visible in the image?
How does field size and shape vary across the image?
Can you identify any irrigation systems or water management features?
What is current stage of crop growth
visible fields?
Are there any visible patterns of crop rotation or fallow land?
Disaster Assessment
What evidence of natural disasters can you observe?
How infrastructure been affected by the disaster?
Can you identify areas at risk of future disasters?
What emergency response activities are visible?
How has the landscape changed post-disaster?
Geologi cal Features
What major geological formations are visible?
Can you identify any fault lines or tectonic features?
What types of crosional patterns are present?
Are there any visible mining extraction activities?
How does geology influence vegetation patterns?
infrastructure Analysis
What types of energy infrastructure can you identify?
How well-developed is the transportation network?
Can you locate major water management facilities? What patterns industrial development are visible?
How does infrastructure density vary across the image?
Temporal Understandis
What time of day this image captured? What shadows or lighting conditions support your answer?
Which scason is represented in this image? What evidence supports this?
What indications of recent urban development can you identify?
What stage of vegetation growth visible in different areas?
What evidence of water level fluctuations can be observed from shoreline features?
23

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Table 22: Manually designed questions for RS specific applications (Part 2)
Advanced Reasoning
Based on the visible patterns, what are the main economic activities in this region?
How do natural features constrain or enable human development?
What ecnsystem services visible in this image?
How sustainable are the visible land use practices?
What future development challenges might this area face based on current patterns?
Quantitatis Assessment
What is the approximate area covered by different land use types?
How dense is the urban development in different parts of the image?
What is the ratio built-up area to green space?
How fragmented are the natural habitats?
What is the distribution pattern of settlement sizes?
Color Spectral Analysis
What does the variation vegetation color indicate about plant health?
Can you identify areas bare soil based on color signatures?
What do the color patterns in urban areas suggest about building materials?
How do seasonal changes affect the spectral signatures of different features?
Table 23: Instruction for prompting models answer the RS specific questions
You expert remote sensing analyst. Examine the provided satellite image and answer the given question.
Remember to:
1. Start with direct observations
2. Base all conclusions visible evidence only
If you cannot
the question with the available information, please explicitly state what cannot determined explain specifically what prevents you from doing so.
Table 24: Instruction for prompting models to rephrase the questions
You are an expert in remote sensing and satellite image analysis. Transform the following question
into new, more diverse version. The new question should:
1. Test the
concept but approach differently Either increase decrease the complexity
3. Change the context or application domain
4. Use a different response format or analytical approach
Also please task that there just single image. So do not output any temporal question. Moreover, do not explicitly mention the image satellite image.
Original question: "[question]
Generate ONE new question that maintains the spirit of the original hut differs in at least of the
above aspects. Do not explain your choices only output the new question.
Output here:
24


Reviews:
[{"review_id": "L8oxoZ5JTC", "reviewer": "Reviewer_CnMa", "summary": "This paper establishes a novel remote sensing vision-language (VL) data perference dataset, including VL data quality dimensions definition, Image-Caption Preference Dataset curation and Vision Instruction Preference Dataset curation. In addition, a novel VL data quality scoring model ScoreRS is proposed and evaluated in practical applications.", "questions": "1. Subfigures in Figure 2 should be enlarged. They are too small to read now.\n2. The method introduction is almost entirely in text form, with too few diagrams. This is very unfriendly to the readers. Authors should add more necessary figures. For examples, Vision Instruction Preference Dataset section generally introduces three branches, but the only figure is the very small and coarse diagram in the middle of Figure2. The architecture and training phases of ScoreRS doesn't even have a independent framework figure.\n3. The main motivation of this paper is quality assessment, but there is no direct accuracy evaluation of ScoreRS itself. All experiments are actually practical applications of ScoreRS. It is necessary to present the evaluation of pure quality scoring task, including both quantitative and qualitative results. The current experimental content is seriously inconsistent with the motivation of the article.\n4. Too many elements for a length-limited conference paper. Perhaps it is better to remain the most crucial parts, e.g. datasets and ScoreRS. Then compile the complete content to another journal version.", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 1, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1. Insightful and valuable topic\n2. Extensive experiments\n3. High novelty and application value\n\nWeakness:\n1. Poor writing\n2. Serious lack of visualization\n3. Inconsistent  between motivation and expriment settings", "was_revised": true, "final_justification": "4: Borderline accept:", "rebuttal": "**We sincerely appreciate your acknowledgment of our work's innovation, value, and the experimental evidence demonstrating its effectiveness. We recognize that in our effort to present comprehensive and rigorous research, certain details may have made the manuscript challenging to follow, potentially causing confusion for readers. We are grateful for all of your constructive suggestions and provide our item-by-item response below:**\n\n\n\n**Q1: Regarding figure size and additional visualization support for understanding.**\n\n**A1:** Thank you for raising this important concern. We acknowledge that due to the paper's formatting constraints, we consolidated multiple components into a single figure, which inadvertently made Figure 2 too dense and difficult to interpret. We will redesign the figure layout and increase the size of individual subfigures to enhance readability and clarity.\n\nRegarding the ScoreRS training process, while our approach follows standard reward model architecture design (with the exception of our novel 3-stage training strategy), we recognize that additional visual aids would improve the paper's accessibility and completeness. We will therefore add dedicated architectural diagrams and training flow charts to better illustrate the ScoreRS framework and its training phases.\n\n\n\n**Q2: Inconsistency between motivation and experimental settings.**\n\n**A2:** Thank you for this valuable feedback. We respectfully disagree that our motivation and experimental design are inconsistent. Our primary motivation is to develop an automated scoring system to assess the quality of remote sensing vision-language (RSVL) data, which is precisely what ScoreRS accomplishes.\n\nRegarding the evaluation methodology, we believe the most compelling approach to validate data quality assessment is through downstream application performance. By using ScoreRS-filtered data to fine-tune vision-language models and measuring the resulting improvements, we demonstrate the practical effectiveness of our quality scoring system. This application-oriented evaluation directly validates whether our scorer can identify truly high-quality data that enhances model performance.\n\nAdditionally, we did include direct evaluation of ScoreRS's scoring capabilities. Specifically, we conducted quantitative assessment using a held-out validation set (Table 6), where ScoreRS achieved 92.91% accuracy in distinguishing high-quality from low-quality data. We also provided qualitative analysis by comparing the highest and lowest ranking samples identified by ScoreRS versus CLIP (Figure 12 in the appendix), demonstrating ScoreRS's superior discrimination ability.\n\nWe acknowledge that this direct evaluation could be more prominent in the main paper. We will enhance the presentation of these results to better highlight the intrinsic quality assessment capabilities of ScoreRS alongside the downstream application evaluations.\n\n\n\n**Q3: Suggestions for retaining only partial components of our paper.**\n\n**A3:** Thank you for this suggestion. Our goal is to present a holistic framework addressing the natural workflow: How to build an effective quality assessment system (manual or model-oriented )? If model-oriented, what training approach and data should we use? And finally followed by validation through experiments. Each component serves the logical progression of our manuscript.\n\nHowever, we acknowledge that extensive content may overwhelm readers. We will restructure the presentation by: (1) emphasizing core workflow and motivation in main sections, (2) streamlining key components, and (3) moving detailed analyses to the appendix. This maintains framework completeness while improving accessibility for conference readers."}, {"review_id": "egTKilq0Hv", "reviewer": "Reviewer_5qQo", "summary": "The paper proposes a method to score image-caption / image-instruction pairs for a remote sensing dataset. It first collects RS images and then asks various VLMs to generate captions or answer questions for instructions. It then uses GPT-4o to rank these answers on 5 different axes. The best answer/caption is chosen as a positive (and the rest as negative) sample to train a scoring function. The model trained a scoring VLM in multiple stages. This scorer can be used to filter out data to find more useful subsets of large training datasets, as the paper shows, the scorer results in significantly better fine-tuning of CLIP on remote sensing data. Moreover, the paper also shows that the scorer can be used as a reward function for RL training VLMs as well as test-time best answer selection.", "questions": "See weakness.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "# Strengths\n* The paper is very well-written and easy to read. \n* The results are very comprehensive and show the usefulness of ScoreRS in various tasks and for various models in remote sensing. \n* The problem motivated by the paper is very novel in the space of remote sensing imagery.\n* The method for collecting the dataset and training the scorer is novel and interesting. \n\n# Weaknesses\n* The image caption/answer generation, as well as judgments on those responses, are both fully automated and done via automated VLMs. \n    * While the human study shows that humans also select the same response, it is unclear if these responses are the best. i.e., both humans and GPT-4o are still ranking one out of the possible 3 outcomes to be positive. In theory, it is possible that neither of them are actually good enough. As a result, the model cannot perform better than the best of these three models used for generation.\n    * In other words, the scorer is learning from the biases of existing RS-VLMs and general-purpose VLMs. If GPT-4o is good at scoring and understanding remote sensing images/captions/instructions, why do we even need remote sensing VLMs?\n    * Overall, using a training scorer using automatic VLMs to filter data to fine-tune the CLIP model seems a bit circular. \n\n## Not weaknesses per se, but comments\n* Figure 3 could be extended beyond 30% to see if the performance improves further.\n* In discussion from line 30-34. Alongside these three methods for obtaining data for training RS VLMs, another set of approaches that could be cited is getting supervisory signals from auxiliary data like internet images [1]\n * Figure 1, could explain in a little bit more detail what each of those terms means. The appendix has this but could be useful in the main paper too.\n \n\n[1] Remote sensing vision-language foundation models without annotations via ground remote\nalignment., Mall et. al. ICLR, 2024", "was_revised": true, "final_justification": "The authors have resolved the weaknesses and promised to add the new qualitative results. I'm upgrading the rating to an accept.", "rebuttal": "**Thank you for acknowledging our method's novelty, comprehensive validation, and well-written presentation. We appreciate your concerns about our preference dataset construction and provide our response below:**\n\n\n\n**Q1: The candidate responses for generating preference datasets may not be good enough.**\n\n**A1:** Excellent question—this was indeed our primary concern during dataset design. We adopted this approach because even when all responses are suboptimal, relative quality differences still exist (e.g., \"not so bad\" vs. \"bad\" vs. \"terrible\"). Learning to rank relatively better responses demonstrates meaningful quality judgment ability.\n\nIn practice, when fine-tuning vision-language models, we cannot guarantee all training data is high-quality. In scenarios with limited resources to rebuild datasets, ScoreRS can still provide value by identifying relatively better samples, even if the absolute improvement is modest compared to improving the data itself.\n\nWe acknowledge that ideal candidate generation should include more diverse models, sampling strategies, and human-in-the-loop verification—approaches used by major industry players. However, given our resource constraints, we implemented the best feasible solution. We hope this work encourages the community to prioritize data quality and scale our methods further.\n\n\n\n**Q2: Why not directly use GPT-4o as remote sensing (RS) VLMs?**\n\n**A2:** Thank you for this insightful question. While we use GPT-4o for preference dataset judging, this doesn't imply it excels at understanding RS images—many studies demonstrate the GPT series' limitations in RS image understanding [1], [2]. We leverage GPT-4o for judging because we believe that comparing response quality is easier than generating good answers directly.\n\nMoreover, we didn't use RS-VLMs since they have relatively inferior instruction-following capabilities (their largest existing size is 13B, and their fine-tuning datasets usually do not contain data similar to our preference scoring task). Our human verification study also confirms GPT-4o's superiority (92.6% vs. 82.8% for LHRS-Bot-Nova) in judging data quality.\n\nThe motivation for building custom RS-VLMs over GPT-4o stems from cost and privicay concerns. Figure 8 demonstrates that ScoreRS outperforms GPT-4o in RL training while being more cost-effective: a single RL run costs \\\\$ 360 use GPT4o API versus \\\\$ 256 using ScoreRS on rented cloud compute (16 A100s × 8 hours × \\\\$2/card / hour).\n\n\n\n[1] Vrsbench: A versatile vision-language benchmark dataset for remote sensing image understanding. Li et. al. NeurIPS 2024\n\n[2] GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial Tasks. Danish et. al. ICCV2025\n\n\n\n**Q3: Using a training scorer with automatic VLMs to filter data for fine-tuning the CLIP model seems circular.**\n\n**A3:** Using a qualifier model to filter training data is actually standard practice for improving model performance. Once trained, the scorer can filter diverse data sources, enabling flagship models to train on smaller, higher-quality datasets. This approach delivers better performance while being more cost-effective through reduced data requirements, faster convergence, and lower computational costs. We believe this trade-off is worthwhile.\n\n\n\n**Q4: What happens when experiments extend beyond the 30% data saving threshold?**\n\n**A4:** Thank you for this question. We tested extreme thresholds of 20% and 10%. Results are shown below:\n\n| Saving Ratio-Method | Classification | Retrieval |\n| :------------------ | :------------: | :-------: |\n| 30%-ScoreRS         |     70.97      |   27.11   |\n| 30%-CLIP-Score      |     69.98      |   26.36   |\n| 20%-ScoreRS         |     68.44      |   27.01   |\n| 20%-CLIP-Score      |     67.70      |   26.69   |\n| 10%-ScoreRS         |     63.19      |   26.16   |\n| 10%-CLIP-Score      |     62.89      |   26.05   |\n\nThe results demonstrate that data quantity also remains crucial for maintaining performance, particularly for classification tasks. Extreme ratios significantly degrade classification performance, likely because datasets with numerous classes require sufficient samples per class. However, retrieval tasks are more resilient, as high-quality data effectively captures essential text-to-image alignment concepts, maintaining similar performance even under extreme data reduction.\n\n\n\n**Q5: Suggestions for writing and related work.**\n\n**A5:** Thank you for these valuable suggestions. We will expand the main text to include detailed explanations of our scoring system criteria and enhance the related work section by incorporating additional data building strategies for RS VLMs."}, {"review_id": "v4NGX1sa9G", "reviewer": "Reviewer_3m9H", "summary": "This paper presents ScoreRS, a learned scoring model for assessing the quality of remote sensing vision-language data. ScoreRS is trained on a large-scale RS vision-language preference dataset, which will be made publicly available. The model is trained via a standard three-stage training strategy, and is designed to assess the best vision-language pairs across five quality dimensions defined by the authors. ScoreRS is applied to filter data for CLIP fine-tuning and is also used for reinforcement learning and as a Best-of-N (BoN) selector at test time for VLMs. The authors demonstrate improvements over CLIP-score filtering and claim performance gains when utilizing the top-30% of data ranked by ScoreRS.", "questions": "Authors are encouraged to include more RS-specific baselines as scoring models for data filtering (see Weaknesses[1]). Such baselines, if incorporated in Tables 1-2, would significantly strengthen the paper's comparative analysis and clarify whether ScoreRS offers a real advantage. For example, SkyCLIP [2] provides a filtered dataset based on CLIP-LAION-RS scoring, which seems directly comparable to the proposed ScoreRS approach.\n\nComposed image retrieval task (C2I) can be added as a column in Table 2. Prior work [1] has already evaluated CLIP and RemoteCLIP using WeiCom on top of these pre-trained models on the PatternCom dataset. The last two rows of Table 2 can be evaluated similarly.\n\nBeyond RS-specific models, more recent, gereral-purpose alternatives to CLIP-Score, such as SigLIP/SigLIP-2, could be considered as filtering baselines. If any of these experiments are not applicable for technical or other reasons, can the authors clarify why they are not applicable in the proposed pipeline?\n\nTable 1 and Table 2 could benefit by including additional pre-trained models in the evaluation, such as those provided by SkyCLIP [2].\n\nPotential bias in RL. In Reinforcement Learning experiments, ScoreRS is used as a scoring model for data filtering (for the RL-trained models) and then as a reward model. This raises concerns about potential evaluation bias. Can the authors clarify what steps where taken to mitigate this bias optimism toward ScoreRS method?\n\nComputational cost. Could the authors provide the training costs (GPU hours or FLOPs) for each stage of ScoreRS training, downstream fine-tuning and the inference runtime using ScoreRS? This is important for efficiency and feasibility assessments.\n\n[1] Psomas, B., Kakogeorgiou, I., Efthymiadis, N., Tolias, G., Chum, O., Avrithis, Y., & Karantzalos, K. (2024). Composed image retrieval for remote sensing. In IGARSS 2024 – IEEE International Geoscience and Remote Sensing Symposium.\n[2] Wang, Z., Prabha, R., Huang, T., Wu, J., & Rajagopal, R. (2024, March). Skyscript: A large and semantically diverse vision-language dataset for remote sensing. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 6, pp. 5805-5813).", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths \nScoreRS address an important challenge for the remote sensing community, assessing the quality of large-scale vision-language data.\n\nAuthors evaluate the proposed ScoreRS methodology across various tasks and applications.\n\nThe paper is well-written and the pipeline easy to follow, which makes reproducibility feasible.\n\n\nWeaknesses\nThe comparison in Tables 1 and 2 is based only on CLIP-Score filtering and omits data filtering baselines using RS-specific models such as CLIP-RS-LAION, RemoteCLIP, or SkyCLIP (SkyCLIP provides a filtered version of its initial dataset using CLIP-LAION-RS as a scoring model). This makes it difficult to assess whether ScoreRS offers meaningful improvements over existing domain-adapted alternatives. \n\nAlso, newer models such as SigLIP and SigLIP-2 could be explored as alternatives to CLIP-Score.\n\nThe baseline comparisons are insufficient. Table 1 and Table 2 only evaluate filtering effectiveness using the generic CLIP model and RemoteCLIP trained on the full dataset. SkyCLIP’s pre-trained models, which are publicly available, could have been directly included.\nScoreRS is used both as a filter and as an evaluator (in RL experiments), introducing potential optimism bias in favor of ScoreRS.\nIn line 172 the authors state that they use a subset of SkysenseGPT (381K samples). It’s unclear how this subset was filtered.\nTypographical Error in Line 753: The sentence \"We provide serveal samples...\" should be corrected to “We provide several samples…”\n\nOverall, while the framework has potential, is well-executed, and addresses a critical challenge for the remote sensing community, the methodology is primarily engineering-driven, and lacks sufficient novelty. Beyond that, the most crucial concern is the limited comparisons, both in terms of the baselines utilized as scoring models and the pre-trained models evaluated in the downstream tasks.", "was_revised": true, "final_justification": "I acknowledge the authors’ rebuttal and have considered the other reviewers’ comments. The authors managed to address at least partially my concerns. Still the contribution remains marginal for NeurIPS. Nevertheless, I will adjust my rating to a borderline accept.", "rebuttal": "**We sincerely appreciate the reviewer's acknowledgment of our contribution to data curation challenges in the remote sensing community, our well-written and easy-to-follow pipeline, and extensive experiments. Regarding the reviewer's concerns about more diverse comparisons, computational costs, and novelty, our responses are provided below:**\n\n\n\n**Q1: Comparison with remote-sensing specific CLIP models and SigLIP for data filtering**\n\n**A1:** Thank you for your valuable concerns. We aimed to make our comparisons as solid and extensive as possible, and we initially considered which CLIP models to use as baselines. We chose default CLIP based on two key reasons:\n\n1. **Length extension capabilities**: Most RS-specific CLIP models (RemoteCLIP, SkyCLIP) were fine-tuned on datasets with short captions. We wanted to compare ScoreRS not only in CLIP fine-tuning scenarios but also in large vision-language fine-tuning scenarios where captions and multi-turn conversations can be extremely long (such as the VHM dataset we used to fine-tune Qwen2VL).\n\n    CLIP has its extension version Long-CLIP [1] (although we acknowledge it is not official), while SigCLIP and SigLIP-2 have a maximum length of 64 tokens, and other RS-specific CLIP models are limited to 77 tokens (same as CLIP).\n\n2. **Trustworthiness of fine-tuned versions**: Since we demonstrated that current remote sensing vision-language data is relatively low-quality, we were concerned that fine-tuned versions might be biased toward this low-quality data, resulting in inferior performance.\n\nFollowing your concerns, we noted that CLIP-LAION-RS was fine-tuned on an RS subset of the well-curated LAION-2B dataset, and its filtered version outperforms the CLIP-filtered version. Therefore, we curated the RemoteCLIP dataset using both SigCLIP-2 (L/16-256) and CLIP-LAION-RS (L/14), comparing results with our ScoreRS for fine-tuning CLIP (we did not compare LVLM fine-tuning due to reason 1). Results are listed below:\n\n( Saving Ratio=30%, Classification Dataset={NWPU, AID, METERML, WHU-RS19, SIRI-WHU, fMoW, EuroSAT}, Retrievel Dataset={UCM, RSICD} )\n\n| Method        | Classification Avg. @ 1 | Retrieval Avg @ 1 |\n| ------------- | :---------------------: | :---------------: |\n| ScoreRS       |          70.97          |       27.11       |\n| CLIP-Score    |          69.98          |       26.36       |\n| SigCLIP-2     |          68.24          |       26.47       |\n| CLIP-LAION-RS |          70.59          |       26.41       |\n\nFrom the results, although SigCLIP-2 shows better performance on various tasks in general compared to CLIP [2], factors such as data distribution and training recipes may all contribute to performance differences in the remote sensing domain. Therefore, the SigCLIP-2 filtered version shows inferior performance on classification tasks but improvement on retrieval compared to CLIP. On the other hand, the CLIP-LAION-RS filtered version shows better performance on both classification and retrieval tasks compared to CLIP. Nevertheless, our ScoreRS filter demonstrates the best performance among all these baselines.\n\nFrom these results and other extensive experiments, it is clear that although our ScoreRS is larger than CLIP and its variants, it is the first score model in the RS community that can be used to evaluate different types of vision-language data. Additionally, thanks to the strong capabilities of the Qwen2VL backbone, it can even be used to evaluate multi-image vision-language data such as change captions for multi-image.\n\n[1]  Long-CLIP: Unlocking the Long-Text Capability of CLIP. Zhang et. al., ECCV2024.\n\n[2] SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features. Tschannen et. al., arXiv:2502.14786.\n\n\n\n**Q2: Computational cost of our pipeline**\n\n**A2:** We provide detailed computational costs for each component. ScoreRS training uses 8×A100 GPUs with Flash Attention and ZeRO-2 optimization, requiring approximately 2 hours for stage 1, 10 hours for stage 2, and 18 hours for stage 3. Score inference achieves ~1.4 batches/second on A100 with batch size 8, maximum sequence length 4096, Flash Attention, and FP16 precision. Downstream fine-tuning of Qwen2-VL-7B uses 8×A100 GPUs with LLaMA Factory optimizations, taking ~10 hours for stage 1 and ~6 hours for stage 2. RL training employs two nodes with 8×A100 each (one for serving 8 ScoreRS reward models, another for policy training) and completes within 8 hours using VeRL VLLM pipeline optimization.\n\n\n\n**Q3: The methodology is primarily engineering-driven, and lacks sufficient novelty**\n\n**A3:** We appreciate this concern and acknowledge that our work is primarily engineering-driven. However, as you mentioned, we have built a complete framework addressing a critical challenge for the remote sensing community, demonstrating whether, how, and what approaches work effectively in the RS domain and plan to open-source our efforts to benefit the broader community. Also, we believe our preference data construction pipeline and RL strategy offer novel contributions compared to existing work. \n\nIn the current paradigm where engineering and research increasingly work hand-in-hand [2], [3], while novel methods remain essential for community advancement, demonstrating how to make existing paradigms more powerful for specific domains also constitutes valuable contribution. \n\n[2] On the opportunities and risks of foundation models. Bommasani et. al. arxiv:2108.07258, 2021.\n\n[3] A survey of large language models. Zhao et. al. arxiv:2303.18223, 2023.\n\n\n\n**Q4: Question about the source of SkysenseGPT subset**\n\n**A4:** We did not filter this subset ourselves, but directly used the subset provided by the official SkysenseGPT dataset on Hugging Face.\n\n\n\n**Q5: Potential bias introduced from using ScoreRS in both data filtering and RL process**\n\n**A5:** We acknowledge that since ScoreRS is trained on our preference dataset, any bias in the preference dataset propagates to ScoreRS and subsequently to both data filtering and RL training processes. While we recognize this bias is not optimal, our experimental results (Tables 1-5 and other relevant findings) demonstrate its effectiveness.\n\nIt's important to note that both data quality definitions and RL processes for truthfulness, completeness, and helpfulness inherently reflect human biases about what constitutes good data quality and helpful responses. We constructed our preference dataset based on our scoring system to define good data (our bias), trained ScoreRS on this data, and ScoreRS then applied this bias to filtering and RL processes—ultimately proving beneficial.\n\nWe believe that data filtering (beyond basic deduplication and grammar checking) and alignment training inherently involve models learning human biases, which are not necessarily harmful. While we acknowledge our introduced bias is not optimal, we expect future work to build upon and improve our approach."}, {"review_id": "JA97Kr4lJ0", "reviewer": "Reviewer_EhZY", "summary": "Many existing vision-language (VL) datasets in remote sensing (RS) rely on automatic or semi-automatic curation pipelines, often lacking rigorous quality control. This work introduces a scoring model trained on large-scale RS vision-language preference data for automated quality assessment. Extensive experiments demonstrate the effectiveness of the proposed scoring method for data filtering.", "questions": "See Weaknesses above.", "limitations": "Limitations regarding the quality of preference data should be discussed.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1)Data quality is a critical factor in developing more powerful and reliable VL models (VLMs) in RS. This work provides quantitative evidence that current RS vision-language datasets are suboptimal and require improved quality control measures.\n2)The experimental validation is thorough, with sufficient implementation details provided in the supplementary materials. The quantitative results clearly support the effectiveness of the proposed scoring method for data filtering.\n\nWeaknesses\n1)The claim that *\"studies such as VHM… leverage flagship VLMs like GPT-4 or Gemini for synthetic vision-language data curation\"* is questionable. Most referenced works primarily use rule-based techniques to construct their instruction-following datasets. In contrast, recent works like VRSBench[1] and ChatEarthNet[2] explicitly employ VLMs for vision-language data curation. This distinction should be clarified.\n2)RSGPT[3] was released before GeoChat and stands as one of the pioneering VLMs in RS. Its contribution should be acknowledged in the related work section for a more complete literature review. Moreover, recent surveys on this topic could be included.\n3)In Section 3.2.1, the proposed preference dataset relies on GPT-4o as the scoring system. While this automation streamlines data curation, the reliability of GPT-4o as a preference judge remains uncertain. Given that the dataset contains 76K image-text preference pairs, evaluating only 1,000 random samples may not sufficiently validate GPT-4o’s consistency and bias. Additionally, GPT-4o might have been trained on limited RS-specific data, introducing potential biases. The same concern applies to Section 3.2.2, where *Qwen2VL-72B* is used as a preference judge.\n\n[1] Li, X., Ding, J., & Elhoseiny, M. (2024). Vrsbench: A versatile vision-language benchmark dataset for remote sensing image understanding. arXiv preprint arXiv:2406.12384.\n[2] Yuan, Z., Xiong, Z., Mou, L., & Zhu, X. X. (2024). Chatearthnet: A global-scale image-text dataset empowering vision-language geo-foundation models. Earth System Science Data Discussions, 2024, 1-24.\n[3] Hu, Y., Yuan, J., Wen, C., Lu, X., Liu, Y., & Li, X. (2025). Rsgpt: A remote sensing vision language model and benchmark. ISPRS Journal of Photogrammetry and Remote Sensing, 224, 272-286.", "was_revised": true, "final_justification": "Most of the concerns have been addressed and I decide to keep my original score.", "rebuttal": "**We truly appreciate the reviewer's recognition of our method's importance in demonstrating the necessity for improved quality control in remote sensing vision-language data, our comprehensive validation, and sufficient implementation details. Regarding concerns about further discussion of the proposed preference data quality and related work review, our responses are as follows:**\n\n**Q1: Regarding the possible bias and accuracy of using GPT-4o or Qwen2VL-72B as judging models for preference dataset**\n\n**A1:** We acknowledge potential accuracy concerns when using GPT-4o and Qwen2VL-72B for preference judgment. However, we compared these models against domain-specific VLM models as preference judges. Since current large VLMs in remote sensing are only 13B parameters, not fine-tuned for scoring different answers with given criteria, and trained on potentially low-quality data, their instruction-following capabilities as judging models with our scoring system are inferior to GPT-4o or Qwen2VL-72B (in our human validation: LHRS-Bot-Nova 82.8% vs. GPT-4o 92.6%). Although GPT-4o and Qwen2VL-72B have limited RS data exposure, their training on more diverse data distributions and instruction datasets provides sufficient instruction-following and context-dependent answering capabilities for this task.\n\nTo further validate our preference dataset accuracy, we selected 3 groups of preference data from image-caption (IC) and vision-instruction (VI) preference datasets (1K samples each: 500 from ICPD, 500 from VIPD). These 3K samples differ from the manuscript validation experiment. Three human annotators judged whether chosen positive samples were better than negative samples (Yes or No). We computed accuracy to validate GPT-4o and Qwen2VL-72B as judges. Additionally, the 3 groups shared 100 common samples (50 from ICPD, 50 from VIPD) to validate inter-annotator agreement.\n\n|  Accuracy @ ICPD   |  Accuracy @ VIPD   | Human Consistency @ ICPD | Human Consistency @ VIPD |\n| :----------------: | :----------------: | :----------------------: | :----------------------: |\n| 90.06% (1351/1500) | 96.80% (1452/1500) |       88% (44/50)        |        96%(48/50)        |\n\nResults show that both GPT-4o (ICPD) and Qwen2VL-72B (mostly VIPD) align well with human judgment. However, image captions are lengthy and difficult to quantify, leading to varying human preferences.\n\nWe acknowledge this curation strategy is not optimal. Future work could incorporate more complex construction methods, such as RAG with human-annotated judges as data sources and self-reflection mechanisms to improve judgment results.\n\n\n\n**Q2: Regarding more holistic literature review and clarification about data construction**\n\n**A2:** Thank you for your valuable suggestions. In our early discussion of remote sensing vision-language dataset construction, we distinguished different methods based solely on their image-caption datasets, which neglected vision-instruction datasets. We will revise and better clarify these differences. Additionally, we will include more comprehensive coverage of remote sensing vision-language models, their timeline, and relevant surveys."}]

Author rebuttal:

Meta-review: The reviewers propose an automated scoring-based approach for creating a remote-sensing captioning dataset, where a scoring model is trained to assess the quality of image-caption pairs. Reviewers agree on the significance of the problem and the usefulness of the classifier, as well as the preference data that the model is trained on. All reviewers agree on acceptance following the rebuttal.
Decision: Accept (poster)