GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolution
Authors: Fengxiang Wang
Mingshuo Chen
Yueying Li
Di Wang
Haotian Wang
Zonghao Guo
Zefan Wang
Shan Boqi
Long Lan
Yulin Wang
Hongzhen Wang
Wenjing Yang
Bo Du
Jing Zhang
Venue: NeurIPS 2025 spotlight ()
Keywords: Remote Sensing
MLLM
Vision-language Dataset
Ultra-High-Resolution
Primary area: deep_learning
TL;DR: The first multimodal large language model tailored for ultra-high-resolution remote sensing imagery via the largest image-size datasets and efficient token refining mechanisms

Abstract: Ultra-high-resolution (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to two key bottlenecks: (1) limited availability of UHR training data, and (2) token explosion caused by the large image size. To address data scarcity, we introduce **SuperRS-VQA** (avg. 8,376$\times$8,376) and **HighRS-VQA** (avg. 2,000$\times$1,912), the highest-resolution vision-language datasets in RS to date, covering 22 real-world dialogue tasks. To mitigate token explosion, our pilot studies reveal significant redundancy in RS images: crucial information is concentrated in a small subset of object-centric tokens, while pruning background tokens (e.g., ocean or forest) can even improve performance. Motivated by these findings, we propose two strategies: *Background Token Pruning* and *Anchored Token Selection*, to reduce the memory footprint while preserving key semantics. Integrating these techniques, we introduce **GeoLLaVA-8K**, the first RS-focused multimodal large language model capable of handling inputs up to 8K$\times$8K resolution, built on the LLaVA framework. Trained on SuperRS-VQA and HighRS-VQA, GeoLLaVA-8K sets a new state-of-the-art on the XLRS-Bench. Datasets and code were released at https://github.com/MiliLab/GeoLLaVA-8K.

Ultra-high-resolution (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to two key bottlenecks: (1) limited availability of UHR training data, and (2) token explosion caused by the large image size. To address data scarcity, introduce SuperRs-VOA (avg. 8,376x8.376) and HighRS-VQA (avg. 2,000 1,912), the highest-resolution vision-language datasets in RS to date, covering 22 real-world dialogue tasks. To mitigate token explosion, studies reveal significant redundancy in RS images: crucial information is con- centrated in small subset of object-centric tokens, while pruning background tokens (e.g., ocean or forest) can even improve performance. Motivated by these findings, propose two strategies: Background Token Pruning and Anchored Token Selection, to reduce the memory footprint while preserving key semantics. Integrating these techniques, multimodal large language model capable of handling inputs up to 8K x8K reso- lution, built on the LLaVA framework. Trained on SuperRS-Ve -VQA and HighRS- Datasets GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolution
Fengxiang Wang', Mingshuo Chen', Yueying Li', Di Wang*4, Haotian Wang',
Zonghao Guo Zefan Wang', Boqi Shan', Long Lan', Yulin Wang";
Hongzhen Wang Wenjing Yang'', Bo Du D Jing Zhang"
College of Computer Science and Technology. National University of Defense Technology, China Beijing University of Posts and Telecommunications, China
School of Computer Science, Wuhan University, China Zhongguancun Academy, China Tsinghua University, China Beihang University, China
Abstract
pilot
introduce GeoL.LaV--KK. the first RS-focused
VQA, GeoLLaVA-SK sets new statc-of-the-art on the and code were released at GeoLLaVA-8K.
Introduction
With the rapid development of Earth science, the collection, process, and representation of remote sensing (RS) data have become increasingly important. [1]. Among these data sources, satellite imagery is able capturing extensive spatiotemporal information about Earth's 's surface [2, 3]. sig- nificantly enhancing our geographic understanding of this planet.
Recent advances in multimodal large language models (MLLMs) [4, 5, 6, 7, 8] have signifi- cantly improved visual understanding and reasoning, simultancously facilitating remarkable scien- tific progress in geoscience for handling remote sensing data [9, 10, 11j. However, despite significant progress of MLLMs across both general and RS domains, current models still fall short in addressing real-world RS tasks, especially in the case of ultra-hiih-resoolutio (UHR) scenarios. For instance, even leading models like GPT-40 [12] and Qwen-V VL series [13] are limited up to 4K resolution, re- sulting in limited performance (Accuracy < 0.45) on the KLRS-Bench [ [14], a recent RS evaluation benchmark featuring large image size (e.g., 8K-10K). This gap raises an urgent problem we aim to investigate in this paper:
"Corresponding authors
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Can develop MLLMS tailored for RS data with resolutions far beyond current limits (e.g.. up to 8K 8KX8K)?
Accuracy
51,5
SuperRs-VOL VQQ
8000000000
50
Resolution
Annotation Way: Annotation with GPTs Human Annotation
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48
47.4
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46
45.6
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Figure 1: We introduce S- and HighRS-VQA, the highest-resolution VQA datasets for MILM training. GeoLLaVA-8K, trained on these datasets, significantly outperforms existing MLLMs on resrattigg remote sensing tasks.
In response to this question, we identify two critical challenges that remain underexplored: (1) The lack of image-text training data for UHR RS images; (2) The massive number of visual tokens in UHR RS imagery increases training difficulty for MLLMs. To address the first issuc, we introduce two novel multimodal RS datasets featuring large image sizes: SuperRS- VQQ (about 8Kx8K) and HighRS- HighRS-VQA (about 2Kx2K), To our knowledge, they are the largest image-size RS vision-language datasets date, covering 22 real-world subtasks and significantly surpassing previous RS image-text datasets in both scale and diversity.
For the second problem, from our intuition, the excessive sequence length of visual tokens may lead to two major issues MLLM's training: (1) Expensive Computation Overhead: Current mod- els are not designed to operate at such scales. For example, directly adapting L.LaVA to 8K x 8K inputs results memory overflow. (2) Low Semantic Density: UHR RS imagery contains an abun- dance of homogeneous background tokens, which contribute little useful information. In contrast, semantically rich foreground tokens are sparse and risk being overlooked without effective token management.
To tackle these challenges, we propose two token compression strategies: Background Token Prun- ing and Anchored Token Selection, which focus on token aggregation and refinment to efficiently manage the large number of visual tokens introduced by UHR inputs. Building on these techniques, we develop LeAS.L UHR-oriented RS-specific MILM capable of processing inputs up to 8K resolution. Experiments on large image-size benchmarks (e.g., ., M.RRS-Bench demonstrate that our model outperforms all existing open- and closed- source MI.LMss, setting new state-of-ther aftth-ar..

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In summary, our main contributions are as follows:
We curate SuperRS- and HighRS- two RS image-text datasets covering 22 real- world subtasks for UHR scenes, featuring so far the largest image sizes our knowledge.
(2) We investigate the challenges in training MLLMs caused by the excessive visual tokens in UHR RS imagery. To address the problems of redundant backgrounds and the sparsity of semantically rich objects, propose two strategies for token pruning and selection.
(3) We develop cessing inputs up 8K. Experiments representative UHR RS benchmarks demonstrate its superior performance compared to existing open- and closed-source MLLMM.
the first RS MLLM tailored for UHR seenes, capable of pro-
2 Related Work
Remote Sensing Multimodal Datasets. With the rapid progress of large multimodal models in general domains, the RS field has witnessed significant advancements in MLLM development [9, 10, 11, 15, 16], leading to the emergence of several RS-specific lasion-lnguuag datasets. RSVQA [17] comprises image/question/answer triplets, with questions and answers derived from OpenStreetMap (OSM). RSIVQA [18] generates samples automatically using existing scene clas- sification and object detection datasets. RSSA [19] targets hallucination, while FIT-RSRC [16] focuses object relationship understanding. VRSBench [20] includes 29, 29,614 images, 52,472 ob- ject references, and 123,221 QA pairs. Recent datasets for the SFT stage of MILM training include GeoChat Instruct [9]. ChatEarthNet [21]. VHM sft [22]. FIT-RS [16]. and MMRS- 1M [15]. How- ever, most of these datasets are aggregated from existing sources and contain limited original anno- tations. Critically, their average image resolution remains below 1Kx IK, which is insufficient for real-world UHR RS tasks. Recently. TLLRS-Bench [14] introduces the largest image-size RS bench- mark to date (average 8,500x8,5000 for evaluation purposes only, underscoring the persistent lack of corresponding UHR training data in the RS MILM field.
Multimodal Large Language Model. Leveraging advanced large language models (LLMs) like GPT's [5] and LLaMA [6], MILMs have demonstrated strong visual understanding and reasoning capabilities [23, 24]. Proprietary models such as Gemini [4] and GPT-4o [5], along with open- source alternatives like Qwen- [8]. MnterLLM-XCooppose
[25]. MiniCPM [26]. LLaVA [27], and MiniGPT-4 [7], show competitive performance but are typically limited to input resolutions of 2K-4K. To overcome this limitation, VL-Ve-Next [28] processes images in patches and connects them via global tokens, Monkey [29] and LLaVA-UHD [30] compress patches to reduce redun- dancy, Cambrian [31] uses learnable queries for multi-scale interaction, and SliME [32] applies dual compression preserve global context. In the RS domain, several MLLMs have been devel- oped. For example, GeoChat [9] enables multi-task RS dialogue based on the VLa frame- work [33]. EarthGPT [15] unifies multisensor interpretation tasks by converting RS annotations into question-answer pairs. Meanwhile, LHRS-Bot [10] improves vision-language understanding through multi-level alignment and curriculum learning. Nevertheless, both general-domain and RS MLLMs struggle to effectively handle UHR RS imagery. Universal MLLMs lack domain adaptation and are unable meet the higher resolution requirements of real-world RS applications. Although RS-specific MLLMs incorporate domain knowledge, they still operate at significantly lower reso- lutions, i.e., less than IK, highlighting the urgent need for UHR (8Kx 8K) and domain-aligned MLLMS in the RS field.
3 Dataset
The RS domain currently lacks sufficient UHR image-text training data for MLLM development. To validate this judgment, we firstly unified existing RS image-text datasets with general-domain data in a 2:1 ratio for supervised fine-tuning (SFT). However, we observed a notable performance drop in LLaVA-Next-2K [34] on UHR benchmarks (XLRS-Bene Res-B [14]) in the Tab. 1, which we attribute to the limited resolution of current RS training datasets, highlighting the urgent need for UHR image-text pairs to support real-world RS applications.
To address this, following XLRS-Bench [14], we first manually annotated 12K UHR samples. No- tably, existing ML.MSs such as GPT-4o [12] either encounter memory overflow or generate low- quality outputs when processing UHR RS imagery. Therefore, adopt fully manual annotation
3

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approach in this work. Nevertheless, due to the high time and labor costs, scaling manual annota- tion of UHR data is impractical. To address this, we develop semi-automated annotation pipeline that leverages GPT-4o [12] alongside existing RS detection and segmentation datasets, generating 100K Beeduum-to-high-resone
(MHR, 2K X2K) image-text pairs. To bridge the distribution gap between MHR and UHR data, we further apply an influence-based data selection method built on the LESS framework [35]. Next, we detailed the procedure of constructing datasets.
Source Screening, Our datasets have di- verse and extensive image sources. We fol- low a key principle: to avoid using the same data sources as existing vision-language datasets as far as possible, ensuring diversity and effectiveness in training data. Specif- ically, we utilize datasets such as Deep- Globe [36], STAR [37], FAIRIM 2.0 [38], LoveDA [39], Inria [40], OpenSatMap [41]. HRSCD [42], MiniFrance [3], and DOTA [2]. To minimize redundancy of image-text pairs, we deduplicate images within these datasets and remove overlaps with existing benchmark datasets like XLRS-Bench [14]. Nonetheless, it should be noted that, since generating MHR image-text pairs requires leveraging exist- ing annotations with tools like GPT-4o, the overlap with prior VQA datasets is unavoidable.
Table 1: Pilot Experiments of Dataset. We
keep the original pretraining data of LLaVA-Next- 2K [34] (a variant of LLa [28]) and we only modify the SFT (supervised fine-tuning) data in the second training phase. Accuracy is reported based on results from XLRS- BeSsBBench [14].
Model
TaI Scurce
Da
Volume
Avergee Kannittin
Acc.
LLaVA-No
Meas-N [25]
738K
512x512
44.6
ULMA 2K
512-512
Instrct
121
LLLVA-Nos
VHM
643:638
FIT
1200
512-512
MMRS-1M
908K
409-401
VRSBenchemain (20)
8%.
$12:512
Annotation. For UHR data, we formed team of MLLM and RS experts (Ph.D. holders or candidates) and 30 crowd-sourcing annota- tors (undergraduate and masters students). All samples were manually labeled and rigorously cross-validate over 40 days of annotation and 10 days of verification, resulting in 12K UHR image-text dataset, named SuperRS-Ve VQQ.. For MHR data, developed semi-automated an- notation. Using task-specific prompts and exist- ing annotations (e.g.. bounding boxes in RS de- tection datasets), we generated text via GPT-4o [12]. Despite high token costs (>$1,000), out- puts still needed to be quality-checked by anno- tators.
Recovvting
Perouppion
(a) Super VQA
(b) HighRS- -QQA
Figure 2: Our datasets have the perception and rea- soning dimensions across 22 sub-tasks.
Data Selection Pipeline for MHR Data. We further adopted an influence-based data selection pipeline (see Fig. 3) to improve the relevance of our dataset to UHR downstream tasks and ensure its cultivation of reasoning capabilities for models fine-tuned on it. This pipeline quantifies the exact contribution of each training data sample by measuring the change in validation performance when that sample is removed (i.e., a Leave-One-Our influence score). Concretely, we leverage gradient information from a warm-up fine-tuned model trained on the crude dataset: for each candidate z E Z among training examples and validation examples x E X, we compute
Infsan(2,, X) max cos (Lo(2)). Vlo(z.))
(1)
x
where lo is the negative ligeliiel loss on data sample. The above estimation is a simplified but empirically valid form of Inftuence Function [43], a widely used data valuation method which is first-order approximation of performance change. Intuitively, higher InfsGD indicates removing zi from the training set would cause a larger, more directionally coherent shift in validation loss, and hence that 2; is more influential. Unlike heuristic selection methods based the apparent semantics of VQA pairs, which often require laborious task-specific rule engineering, our approach directly measures data influence utilizing the preferences of fine-tuned model.
We follow LESS [35] for a practical and efficient implementation of our method. The main challenge of using influence-basee based methods is gradient storage consumption, despite the Hessian calculation already omitted in our formulation. Therefore, only gradients of LoRA adapters are calculated. Moreover, the gradients are projected to 8192-dimensional subspace via fixed random matrix. We

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Original
Gradient Datastore
Datasets
LoRA Gradient
Volidation
Select deta with the Aighest simierity
Random mapping
Setract groskent
Adopher
fexturez
Conyute
Pretrained
up
Checkpoint
Festures
C.i
Datk
Data Selection Pipeline for MHR Data. In step we train warmup model on the crude dataset to acquire gradient features for both the training and validation sets. step 2, we match training data candidates with the validation and select the most influential samples.
Figure 3:
note that this leverages the Johnson-Lines Lindenstrauss Lemma [44] that random projection preserves the inner product of gradients, and the recent empirical validation [45] for data selection. The cosine similarity is used instead of the original inner product in common influence definitions because the gradient norm of language model may have numerical biases [35]. After profiling gradients of the
crude training set and the validation set, we rank and retain the top 70% most influential samples for downstream fine-tuning.
Statistics. Fig. highlights the advantages and key details of our dataset, which offers the highest resolu- tion among all RS image-text training datasets to date. Fig. 2 visualizes the capability dimensional coverage of both datasets. It can be seen that our datasets span wider range of task dimensions, showing strong align- ment with real-world scenarios. Finally, we summarize the key statisties of our datasets in Tab. 2, including av- erage question/answer lengths, the diversity and quan-
Table 2: Main statistics of our datasets
Statistic
Number
Total questions
81,367
SuperRS- VQQ
12,228
HighRS-
69,139
SuperRS-VQ2. RV-QAA
Multi-image questions
150
Average question length
128
Average answer length
22
tity of annotated objects, average resolution, and other relevant attributes.
Object category
18 (Coarse)
Object size (pixel ratio)
0.14%
Average Resolution
8,376 8,378
4 Method
HighRS-VQA
AAerage question length
172
Average answer length
Before training on our datasets, investigate how
Object category
the Low Semantic Density characteristic, i.e., excessive
Object (pixel ratio)
1.02%
length visual tokens caused by UHR RS imagery, af-
Average Resolution
2,000 1,912
fects the performance of ML.MMs Specifically, Sec- tions 4.1 and 4.2 separately analyze the low information density of RS imagery from two views: Section 4.1 reveals the redundancy of background tokens and their negative impact MLLM mod- eling, while Section 4.2 explores the correlation between scarce objects visual tokens. Section 4.3, we propose targeted solution overcome these challenges.
4.1 Overwhelming Background Tokens Hinder MLLM Fine- Tine-Tuning on RS Data
Do background tokens dominate UHR RS imagery?
In order to learn about a holistic recog- nition for the background ratio in UHR RS scenes, we conduct preliminary ex- periments, where the LLaVA-1.5 [33] is employed. Specifically, we randomly selected 100 images from the XLRS- Bench [14], covering natural backgrounds such as sea surfaces, forests, and fields, to provide diverse set of test samples the evaluation of background redun- dancy. Then, we extract 64 x64 small im- ages through non-overlapped sliding win- dows, and sequentially input each into the
(s) objects
Figure Examples tokens and their positions that the logit lens yields in the late layers.
5

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model. We evaluated the background rate of the UHR RS scene using two methods, following the previous work [46]: Generative Deseription: We prompt the model with "Describe the image" and analyze the generated description to extract key information. Binary Polling: We ask the model "Is this image mainly background?" where the background defined as low-information natural areas (e.g,, sea, deserts, vegetation), excluding artificial structures (e.g. buildings, roads, urban areas).
We developed multi-level semantic parsing framework to precisely quantify MLLM's responses. The framework separates the description from the evaluation content and processes it in five steps: Urban Semantic Recognition, Human-made Structure Analysis, Natural Element Classification, Se- mantic Categorization, and Probability Gradient Quantification. Details are shown in the appendix. Results show background coverage in RS images reaches up to 73.14%.
To better understand the semantic information of visual tokens, we use the logit. lens technique [47]. For each layer, we decode the activation at each token position via unembedding. Details are shown in the appendix. With the logit lens technique, we found that in large background areas, only a small portion of the information the visual tokens is effectively mapped specific semantic scenes, while most of the background tokens do not align with clear semantic representations (Fig. 4(b)).
Do background tokens in RS imagery hinder MLLMs from effectively modeling UHR satellite images?
Then, feed the whole UHR RS imagery into MLLM, where the LLaVA-Next-2K [34], a variant of LLaVA-Next [28], is adopted to support higher resolution as far as possible. Initially, we directly perform the inference XLRS- Bench [14], whose images have an average resolution of 8K x 8K.
Naturally, the resulting long visual token sequences lead to out-of-memory (OOM) errors, aligning with the first challenge presented in Section
Average Pooling
OOM Error
X
9,000-0,100
LLaVA-Neet-X
5,000
Visual Encoder
2 Prening keroand Tokes Pooling
LLaVA-Nert-9K
412
Toard cessantii simileriy badkgrend.
1,00
LLaYA-Naai-KK-Pauun
Presing 50% takess
indicates the pruned background token.
Accuracy
Figure 5: Halving the background tokens by half even resulted in improved performance. To address the OOM issue, we increase the pooling layer size from the default 2 2x2 2 to 6x6, al- lowing 2e process 9K 99 resolution, called LLaVA-Next-9K To further reduce redundant background information, we use background lating semantic similarity between visual tokens and typical background terms (e.g., ocean, desert), we score and rank tokens, then discard the top 50% most redundant ones. The process involves: (I) extracting embedding vectors for typical background terms; (2) calculating each token's highest similarity score with these embeddings its background score; and (3) ranking tokens by score and removing the top 50%. This approach reduce the background token count. The result is referred to as LLavA-Next -ew--Neeet--K-Punnin 9K-Pruning
token selection strategy. By calcu-
As Fig. shows, VAAVA-Next-9 underperforms due to its lack of exposure to 9-rrsolution train- ing data, emphasizing the need for UHR RS datasets. Surprisingly, despite LLaVA Next-9K-Prunnnn uses only half the tokens of LLAVA-NexI-9K, achieves better accuracy. This suggests that exces- sive background tokens not only introduce additional computational overhead but also impair per- formance. These findings underscore a critical insight: reducing background redundancy critical to perform effective modeling under UHR RS images.

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From the above two experiments, we confirm that at the token level, RS images exhibit significant background redundancy, where key target information constitutes only a small fraction of the image, while most regions lack explicit semantic significance. This presents major challenge to the efficiency of multimodal models.
4.2 Scarce Object Tokens Drive MILM Fine-Tuning on RS Data
After recognizing the background redundancy of UHR RS imagery in visual tokens, we turn to the second key aspect of understanding RS's low semantic density: the localization of critical informa- tion.
Whether essential information is concentrated in small targets and captured by corresponding visual tokens?
We conduct ablation experi- ments to test whether the fore- ground information is concen- trated specific visual tokens. By removing selected tokens and observing the drop in recog- assess
Ask object identification questions
patties
(3) Generative
Dosor be the [bbok] region in the image"
Ichan
Peeduum
Ablete visual takens different ways
VQA or
nition performance, The im- their importance.
or
Or
X K
K
OF
ages are sourced from XLRS- Bench, with two pre-processing steps applied to ensure reliabil- ity: High signal-to-noise sub- images and Hallucination con- trol. Details are shown in the appendix. Following this proce- dure, the final dataset includes 1,189 VQA pairs. Fig. 6 pro- vides an overview of our abla- tion. Details are shown in the ap- pendix.
Yooad fuliara
Cojjct solitin
de
LLM
Compare befora ablation
Before Ablation
posit midole
image shows trees-
Figure 6: Overview of object tokens ablation experiments. We ablate some visual tokens that potentially contain object- specific information, @ prompt the model describe the image, or answer objectspecific questions, then @ measure the impact of token ablation by calculating the percentage of initially correct object identifications that become incorrect after ablation.
We define the token subset S for ablation using four settings: (1) Object Tokens: Tokens aligned with image patches that originally contain the target object; (2) Object Tokens with Buffer: Object tokens along with their surrounding (3k Register Tokens: Tokens whose norms deviate by more than two standard deviations from the mean, corresponding to the register tokens identified as encoding global image features [48]; (4) Random Tokens: baseline which n tokens are randomly ablated.
Tab. 3 shows that ablating object tokens significantly hinders the models ability to recognize targets. Gener- ative decrease and VQA decrease experiments fol- lowing the previous work [46]. A larger percentage drop performance indicates greater impact of the ablation, meaning the model is more likely to answer incorrectly, thus suggesting that the ablated tokens con- tain more localized object information. Notably, with comparable number of ablated tokens, removing ob- ject tokens consistently results in a larger performance degradation than random ablation, highlighting the pre- cise localization of object-specific information. logit lens analysis [47] further reveals that visual tokens for scarce objects efficiently converge accurate semantic representations (Fig. (a)). Collectively, these findings underscore a key insight in RS: object tokens not only encode critical information but also align closely with actual visual features.
Table 3: Performance degradation after token ablation. "Avg. token counts" de- notes the average number of tokens ab- lated per image for LLaVA-1.5.
Ablation Type (Avg. Token Count)
Generative
VQA Decrease (%)
Object (26.5)
34.8
+1 Bulfer (50.2)
44.8
32.6
Buffer (81.8)
4066
Register
9.7
Rundom (5)
Randum (30)
14.5
4.4
Random (50)
17.5
7.1
Random (80)
25.6
9.9
7

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4.3 Background Token Pruning and Anchored Token Selection
Through the above analysis, we have identified the Low Semantic Density challenge in UHR RS imagery, characterized by both background and foreground tokens, which poses a significant obsta- ele training MLLMs. Based on these findings, we argue that an RS RS-specific MLLM capable of efficiently handling UHR imagery is both necessary and feasible. As shown in Fig. 7, we propose a two-step token selection strategy for background and object tokens in RS imagery. Specifically, building on our two high-resolution RS datasets: SuperRS-VOL and HighRS- we SFT exist- ing MI..Ms, where we select L.LVV-Nexx-2KK [34], to create RS MLLMs. In practice, we initialize from VA-exxt--- general-domain pretrained weights.
Step Pruning Background Token via Semontic Affinity
Step Anchored Token Selection
Eterction
After Iterations
Most similor token
Tekerd overlppiing
[CLS] othent lan for small forget
Se lecttiig hooene target information
Number of iterations
Bellegg the bectigiccnall
18
1B
15
lupa The baiigrad
Figure 7: Our two-step method processes visual tokens during MILMS SFT stage, between the visual encoder and the projection layer.
Background Token Pruning via Semantic Affinity. To address the redundancy of background tokens in visual token sequences, we propose an adaptive token clustering strategy for compressing background tokens. Specifically, we construct token-to-token associations by assigning each token p = (u, v) to a neighboring token with probability q_s(p). Rather than applying this globally, we restrict associations to the local neighborhood N ensuring:
Σ (p) =1
sENp
After performing this computation once, we iteratively reapply the same process to the selected tokens for N steps, gradually forming an initial cluster. We assume that background tokens, such as those containing ocean features, exhibit high similarity and thus are suited for clustering. We perform this process for all tokens. Note that the clusters formed during later iterations may overlap with those from earlier steps. In such cases, we merge the overlapping clusters into single one. This adaptive clustering strategy allows us to effectively group similar background tokens, e.g., ocean- related tokens, into one irregular cluster, and forest-related tokens into another, thus compressing redundant visual information more effectively.
Anchored Token Selection for Scarce Object Retaining, To prevent small-object or otherwise informative tokens from being lost in a background-ordee -orienttd pruning stage, we introduce Anchored Token Selection (ATS). ATS leverages the attention map formed between the pretrained VIT's [49] [CLS] token and the remaining image tokens after background pruning. Tokens receiving higher [CLS] to patch attention are deemed more semantically important and are kept, as they likely cor- respond to informative objects. Specially, the attention map a(cLs) E R Ixn from the [CLS] token
ZiCLs] E R to other patch tokens Z, E RRa is computed by
ZiCLs] Wo Wv) = Softmax
qICLS) K Vd
OrCLS]
Softmax
Vd
(3)
where n the number of remaining image tokens, is the dimension of hidden states, and WQ. Wy are the query and key projection matrices of this encoder layer. Note that we utilize the attention map at the second-to-last layer of the visual encoder (i.e., the output layer of CLIP-ViT in LLaVA- Next [28]). Given compression ratio we calculate the final number of tokens to retain as R = n r. Ultimately, we select the top tokens with the highest attention scores from the attention map d[css) obtaining the final retained tokens Z' E IBRxx After passing through multi-modal projector g, the remaining image tokens are concatenated with language instructions H, and fed into the language model with trainable parameters fo to generate the response X, with L text tokens in the auto-regression manner, where the probability of X,, is computed by p X (g( ()),, H.) =
Il (o Z.). H., X X
8

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5 Experiments
We perform SFT training on the SuperRS-VQA and HighRS- VQA datasets, with a brief overview of the training details and results this section. Exploratory and ablation studies are presented in Section 3 to clarify the research motivation. Additional details, ablation studies of dataset and method, as well as case analyses, are provided in the appendix.
5.1 Main Results
Experimental Setup. We use XLRS-Bench [14] for evaluation. The MLLMs evaluated on XLRS- Bench are grouped into three categories: (a) open-source MILLMs; (b) closed-source MILMSs and (c) the specialized RS model. For fair comparison, we used zero-shot setting with uniform prompts for all MILMs, including our work. The appendix details the architecture and parameter sizes of each open-source MLLMs, and includes additional results across various settings. Except for GeoChat which was evaluated using its native framework, all other models were evaluated using AMMs-Eval [50, 51]. Following ELRS-Benchs [14], we evaluated the accuracy and reported of L-1 dimension for the VQA task, with L-3 and L-4 results available in the appendix.
Table 4: Experimental results on the perception and reasoning dimensions on XLRS-Bench with models ranked by average performance. 'Avg.' represents the average accuracy across sub- The full name of sub-tasks can be found in the appendix.
tasks. We mark the highest score in red
Method
Perception
Reasoming
Sab-taoks (1-3 Capabilitty
OC
RC
OLUC
REUC
OCC
OCT.
OMS
OSR
AD
ECR
RP
RCCD
CCR
Avg.
Remete Sonsing MLLMS
GooChat [?]
16.7
20.0
2.0
23.0
21,1
16.8
35.0
24.2
33.0
43.0
10,0
21.0
22.9
Clevod-onne MLLMy
[12]
15.0
32.4
GPE-4n-minl [52]
23.3
1900
59.5
40.9
31.0
6.7
Clande 3.7 Sannet [53]
27.6
23.7
17.
68
30.5
28.9
63.6
27.6
64.8
78.4
34.5
27.8
32.6
40.5
Gemini 2.0 Flash
41.7
45.0
38.0
73.5
34,5
27.6
61.7
32.0
73.0
82.0
43,0
30,0
$1.0
48.7
Open-snance MLLMs
MeemLMMXXommpoe--255 2.5
27.8
26.0
36.7
39.1
LLaVA Next (28)
26.7
40.0
5.0
67.0
28.8
32.8
66.7
30,0
69.0
78.0
27.0
25.0
36.0
41.7
LLaVA.OueVsttoo.. [55]
25.0
2.0
09.5
25.9
35.3
25.2
43.3
42.9
IntemV133-58 [56]
40.0
39.0
10.0
71.5
44.5
50.8
65.00
25.2
77.0
82.0
36.0
21.7
50.0
45.6
(wem2-VL-70 [15]
26.7
40.0
11.0
73.0
35,9
34.6
61.7
31.8
70.0
81.0
35,0
46.7
48.0
45.8
LLaVA Oue Vision 72B [55]
33.3
33.0
15.0
72.5
20.3
36.3
66.7
35.5
74.0
83.0
28.0
36.7
43.0
46.0
1nccaVL2.5-8 (57)
10.0
33.4
46.2
Qum2.5-V1.78 [S8]
33.3
40.0
31.0
77.0
40.6
40.5
66.7
36.2
68.0
72.0
27.0
38.1
45.0
47.4
ImommVL.3-788 [66]
33.0
Qwen2.5 VL- [:8]
33,3
47.0
39.0
80.0
45.3
42.1
65.0
34,0
71.0
74.0
37.0
43.5
42.0
50.2
Goo..aVAA-SK (Our)
26.7
38.0
69.0
41.6
31.6
65.0
67.0
78.0
50.0
52.00
51.5
After fine-tuning SuperRS- and HighRS-VQA, our La delivers outstanding performance across various evaluation tasks. It not only outperforms domain-speciff models but also surpasses all existing open- and closed-souree models, including the latest Qwen2.5 and Intern Remarkably, with just 7B parameters. GeoLLaVA VL-72B, the largest and best-performing open-source MLLMs. This impressive gain stems from the high-quality dataset and the targeted compression strategy designed for the low semantic density of RSS imagery.
Main Results.
even outperforms Qwen2.5-
5.2 Further Analyses
Effect of High-Resolution Data To- ken Optimization Strategies We conducted an ablation study to analyze the effects of high-resolution data and token optimization strategies. As shown in Table 6, using high-resolution datasets (SuperRS-VOL VQA and HighRS- VQA) already improves performance over the baseline. When combined with Back-
Table 5: FLOPs and latency of GeoLLa VA-8K under different compression ratios.
VA-SK
Compression
Tokess/rri
Viual Tikess
TFLOPs
Lateney (stmn))
Avg.
(O0M)
14.k
198.06
10%
149.06
2.05
50.1
48X
7.1k
100.11
1.09
50.1
NEX
300
$1.13
1.46
49.7
ground Token Pruning (BTP) and Anchored Token Selection (ATS), the model achieves the best average accuracy of 51.5%. This shows that while high-resolution data enhances visual understand- ing, most performance gains come from token optimization, which focuses attention on semantically important regions.

---

Table Ablation study on the effect of high-resolution datasets and token optimization strate-
gies.
Subtasks
0C
RC
OLUC
REUC
OCC
OCT
OMS
OSR
AD
ECR
RP
RCCD
CCR
Arg
LLaVA (Baseline)
28.3
47.0
3.0
62.0
42.0
34.1
66.7
27.2
73.0
15.0
34.0
48.3
40.0
44.7
High Resolution Dataset
25.9
41.0
50.0
68.0
40.4
28.5
64.9
33.4
58.0
77.0
57.0
45.0
30.0
49.1
Daiuset BTP(OOM)
00M
Dotsset ATS (Ge)LLaVA-SS))
26.7
38.0 49.0
31.6
65.0
35.0
67.0
78.0 66.0
50.0
52.0
Computational Efficiency under Different Compression Ratios We evaluated the computational efficiency of GeoLLaVA-SK by comparing FLOPs and inference latency across different token compression ratios. As shown in Table 5, higher compression ratios greatly reduce visual tokens and computational cost. At 24x compression, the model processes about 14K tokens with 198.06 TFLOPs and 2.17 s/image latency. Further compression to 96x lowers FLOPs to 51.13 and latency to 1.46 s/image, demonstrating the scalability and efficiency of our token optimization framework.
Small Object Preservation and CLS Attention under Token Compression. We analyzed the im- pact of token compression and CLSS-bsee attention on small-object scenes using a subset of XLRS- Bench defined by COCOs area criteria. Small objects were defined as occupying less than 5% of the image area, and categorized into three levels: Extremely Small (<0.1%), Very Small (0. (0.1%-2.0%) and Normal Small 22.0%....%). dedicated subset of 1,570 samples covering six tasks (classifica- tion, color, motion state, counting, spatial relations) was curated. As shown in Table 7, GeoLLaVA- 8K outperformed LLa VA-Next across all tasks, with notable improvements in spatial reasoning, counting, and motion state. These results show that token optimization effectively preserves small- region semantics and reduces information loss. The CLS attention mechanism generally focuses on task-relevant small objects but occasionally favors single dominant region, suggesting room for further improvement in mujti-object focus and attention balance.
Table Performance comparison on the small-object subset of XLRS-Bench.
Dataset
Object Classification
Objject Color
Object Modion State
Regional Counting
Overall Counting
Objject Spotial Relationship
Overall
HaMAA-Nst
38.6
29.4
62.0
35.0
22,5
20.0
34.1
GooLLaVA SK
45.3
34.4
7000
40.0
32,5
35,0
40.3
Generalization on External Datasets To evaluate the generalization of GeoLLaVA-SK, we tested it
Table Generalization performance on LRS- VQA.
on the new ultra-high-resolution benchmark LRS-
VQA [59]. Since LRS-VQL only provides open- ended QA annotations, converted it to multiple- choice format using an LLM [12] to generate three plausible distractors per question, followed by structural validation.
Method
Accuracy (%)
Evaluntion Format
LLaVA-Next [28]
55.07
MCQ MCQ
GeoLLaVA (ours)
$6.28
GeoLLaVA- 8K achieved 56.28% accuracy, outperforming LLaVA-Next [28] (55.07%) by 1.21%, confirming strong gener-
alization new datasets and formats.
Conclusion
In this paper, we addressed two fundamental challenges in scaling vision-languar models to UHR RS imagery: the lack of suitable training data and the computational burden of token explosion. To tackle these issues, we introduced two new UHR image-text datasets, SuperRS-
and HighRS- VQA, which greatly expand the data available for vision-language tasks on UHR RS images. We also proposed two novel token-efficient strategies: Background Token Pruning and Anchored To- ken Selection, which effectively reduce the number of visual tokens processed from 8K-resolution images while preserving essential information. Building on these contributions, we developed GeoLLaVA-Ske the first RS-specific MLLM that can directly handle inputs up to 8Kx8K resolution. benchmark, outperform-
GeoLLaVA--88 -8K achieved state-of-the thetherrrt art performance on the ing both open- and closed-source MLLMS and demonstrating the effectiveness of our loken-efficiee approach. These results underscore the value of domain-adappe vide a new foundation for high-fidelity image understanding in RS.
token-efficient modeling and pro-
Limitation. We have primarily focused on optical satellite imagery; evaluating GeoLLaVA on other sensor modalities (e.g., synthetic aperture radar or multispectral images) will be important to ensure broader applicability.
10

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Acknowledgements: This work was partially supported by the National Natural Science Foundation of China (No. 62372459, No.62376282, No.62225113 and No. 624B2109).
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A Appendix
A.1 Overview of the Appendix
This appendix supplements the proposed GeoLLaVA-SK and our datasets (SuperRS-VQA and HighRS- VQQA) with details excluded from the main paper due to space constraints.
The appendix is organized as follows:
Sec, A.2: More implement details of GeoLLaVA-8K.
Sec. A.3: The analysis L-2 capability GeoLLaVA-8K.
Sec. A.4: Ablation studys of Gao..L.V
Sec. A.5: More implement details of Pilot Experiments.
Sec. A.6: Visualizations of samples and challenging cases.
Sec. A.7: Datasheets for the SuperRS-VOL VQA and HighRS-VQA dataset.
Sec. A.8: Discussion on limitations and societal impact.
A.2 More Details of GeoLLaVA-Sk
g Bocko Semantic Aftinity
obc The lenge caoe deo oven
Visual Encoder
MLP
LLM
8,064x8,064
1915
Step Aradicsed Tickes fion
Figure 8: Overall training framework. We use SuperRS-Ver VVQ and HighRS- VQA datasets for SFT. Our two-step tokens compression method is peformed between visual encoder and projection layer.
GeoLLaVA-8k is developed via full-parameter supervised fine-tuning (SFT) of LLaVA-Next-7B [28] SUP and HighRS-VQA datasets (see Fig. 8) We first expanded LLaVA- Next's capacity from processing 7G7 visual grids (CLIP's default 336(E336 pixels per grid, =22K resolution) 240E24 grids (s88K resolution) by directly adjusting the relevant hyperparameters.
To address the explosion of visual tokens, we introduced two-stage token-efficient mecha- nism. As described in Section 4.3, the original 20E2 pooling was replaced with clustering- based strategy to merge and compress background tokens. We then applied Anchored To- ken Selection to further reduce tokens while preserving informative tokens. This approach achieves significantly higher compression: reducing 576 tokens per grid to 24 representative ones, achieving 24GE compression rate, which clearly
ing. outperforms the 40E reduction original 2CE2 pool-
Table 9: Training Configuration of Lav
Tab. presents the key training configuration of our GeoL.aVA-8K model. The model is trained on 81K
Configuration
Parameter
Resolution
8,064 8,064x8,066 8,064
UHR geospatial image-text pairs. We use differ- ent learning rates for the visual components (1c-6) and the projection layers interacting with the LLM (5e-6), and optimize the model using ZeRO-2 paral- lelism with a batch size of 32 for one training epoch.
Dataset
81,367 SSppeRRS-VQA-HighRSS
Batch Size
16
LR: vision
le-6
LR: proj, LLM
5e-6
ZeRO stage
ZeRO
Fpoch
Tab. 10 presents the detailed structure of XLRS- Bench, the benchmark dataset used to evaluate Geol LaA--K The dataset is hierarchically organized into two Level-1 categories: Perception and Reasoning, each comprising multiple Level-2 and Level-3 sub-tasks. All Level-3 sub-tasks are formatted as VQA with multiple-choice questions, totaling 3,040 samples. Perception tasks assess
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fundamental visual understanding, while reasoning tasks evaluate higher-level cognitive abilities in geospatial contexts.
Table 10: Characteristics of XI.RS-Bench, used as the benchmark for evaluating La--L. The full names of the Level-3 task abbreviations are also provided.
1.1-bakk
I.B-Taak
Abbr
Ammodation Fermat
Number Namples
Ancwer Type
Counting
VQA
Muttple (holot//B/CCD)
Counting
Regional Counting
Chelo//M//CD)
Oveual Land Use Clasifiction
OLUC
VQA
100
Multjle Chokoe/A///CID
Scene Clssiitcation
Regional Land Use Claoilsztion
BLIXC
VQA
200
Mulijée (beies(A///CDD
Pesception
Objest Spoiial Reteiumehip
Objac Spaiso Rebiicendi
O8R
VQA
Mulipli (heks)//B6C1))
Mittpio (1611
Object Propenies
OCL
Muttill (helotA/66C
Object Motion Stute
OMS
VQA
Multiple Choice//B for YesNo))
Roste Plasning
Rouse Plassing
RP
VQA
100
Muliçic CheCce/AB//ID))
Asimaly Reasoting
Anomay Detection and Interpetacins
AD
VOA
Mulijpic Cheiks(A//rC))
Recorinng
Envincencuial Conditun Reasceing
Chelss(A///C1)
Comples Resansing
Carrring with Complea Rewering
CCR
VQA
Miltplo Delaa
Spateterrporal Reasoning
Regional Counting with Charge Dinecion
RCUD
VQA
Mitipee CholoeA////UD)
A.3 Sub-tasks (L-2 capability) Results on GeoLLaVA-8K
Table 11: Experimental results on the perception and reasoning dimensions of VQA tasks, with models ranked by average performance. 'Avg' represents the average accuracy across sub-tasks.
Method
Perception
Reasoning
Sub-tasks (L-2 Capability)
Counting
Scene Clasdfication
Objject
Object Properties
Planning
Anomaly Reasening
Complex Resssing
Spatintemporal Reasoning
Spatial Relationship
Romose Smaning MLLMs
GooCTut
10.0
Closed source MLLMs
GPT-40 [12]
28.5
GPT-lo-ming [52]
24.2
39.3
23.6
45.6
29.0
71.0
50.5
6.7
Clande 3.7 Somnet [53]
25.2
42.9
27.6
41.3
34.5
64.8
55.5
27.8
Gemini 2.0 Flash [c]
43.3
55.8
32,0
41.3
43.0
73.0
66.5
30,0
Open-nouryy M77.Ms
InterLM XComposer 2.5
31.8
37.5
26.0
22.1
41.0
72.0
64.0
36.7
LLaVA-Nest [25]
25.0
ILaVA-OmnVissimm
24.0
InterVL 228(55]
1laNAAChneMssinn2220
35.7
28.0
36.7
37.7
43.5
21.6
44.6
34.0
73.0
53.0
50,0
1atemaLL3-888 [SG]
39.5
40.8
25.2
46.8
36.0
77.0
56.0
21.7
Qwen2.5 5-VL--7B [S8]
33.3
42.0
31.8
44.0
35.0
70.0
64.5
46.7
Qwen2.5.
VL-72B VL-
[58]
36.7
54.0
36.2
49.3
27.0
68.0
58.5
38.3
GaolLaVVAAK (Our, 7H)
32.3
59.0
35.0
46.1
66.0
67.0
65.0
50.0
This section highlights the performance of MILLMs across L.-2 capabilities, while the related exper- imental results are shown in Tab. 11.
Superior Performance in Local Parsing
GeoLLaVA-8KK superior performance tasks such as Object Properties (46.1, surpassing the re- cent Owen2.5-VL-788 and Object Spatial Relationship (35.0, only lower than 72B models) demon- strates its ability effectively parse local details in UHR imagery, laying solid foundation for comprehensive and accurate ground object understanding. In UHR images, objects often exhibit substantial variations (e.g., buildings or vehicles in different colors), with diverse distribution pat- terns. Geol LaVA-8K effectively preserves and leverages these subtle yet critical visual cues under challenging conditions, leading to superior analytical performance.
Robust Capability in Holistic Understanding.
GeoLLaVA-8K top performance in Scene Classification (59.0, 1st) and Complex Reasoning (65.0, tied for 2nd) highlight its ability to capture core semantics and underlying logic from UHR imagery macroscopic level. For example, the model can accurately determine regions overall function, assess the stage of an engineering project, or infer plausible causes of anomalous phenomena by integrating rich visual cues, which essential for unlocking the full potential of UHR imagery.
Strong Potential in Dynamic Reasoning The most distinct advantage of GeoLLaVA lies in its ability to finely comprehend dynamic processes. Its top performance in Planning (66.0, 1st) and
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Spatiotemporal Reasoning (50.0, Ist), highlights its effectiveness in modeling long-range depen- dencies for reasoning. This capability essential for applications that require preeise spatial and temporal understanding, such as urban expansion analysis, disaster response planning, and dynamic monitoring of natural resources, which are challenging for existing models.
A.4
Ablation studys of GeoLLaVA-8K.
Table 12: Results of ablation experiments on compression rates. 'Avg.' represents the average
accuracy across sub-tasks. We mark the highest score in red.
Compression Ratio
OC
RC
OLUC
RLUC
OCC
OCL
OMS
OSR
AD
ECR
RP
RCCD
CCR
Avg.
16
00M
28.3
39.0
46.0
65.5
42.3
34.1
65.0
35.6
64.0
74.0
63.0
46.7
50,0
50.3
49.0
69.0
31.6
35.0
67.0
78.0
66.0
52.0
51.5
To investigate the key components of the methodology, we conducted comprehensive ablation stud- ies.
Visual Token Compression Strategy. The initial ablation study, with detailed results in Tab. 12, focused on optimizing the visual token compression ratio. We evaluated three settings: 16, 24, and 32. Training with a ratio 16 frequently led to out-of-memory (OOM) errors, even on multi-GPU setups (8 or 16 GPUs). Although the ratio of used fewer tokens, resulted lower accuracy. In contrast, the 24-token setting achieved the best average accuracy (51.5) and enabled stable training on single node with GPUsstriking an effective balance between performance and efficiency.
Table 13: Results of ablation experiments on datasets. 'Avg'' across sub-tasks. We mark the highest score in red
represents the average accuracy
Datasets
OC
RC
OLUC
RLUC
OCC
OCL
OMS
OSR
AD
ECR
RP
RCCD
CCR
Avg.
VRSBmec--min [20]
13.
30.1
36.7
23.6
70.0
35.0
SQA
200
37.0
34.0
41.4
30.3
65.0
33.4
79.0
63.0
46.7
56.0
49.4
SuperRS- YQA HighRRS-QQA
26.7
35.0
52.0
Impact of Training Data Composition. The second ablation study, summarized in Tab. 13, as- sessed the contributions of different data sources to the final model performance. We compared three primary scenarios:
(i) training solely with the VRSBench--rain dataset (which contains 123,221 synthetically gen- erated samples);
(ii) training solely with the SuperRS- -VQA dataset (which contains 12,228 high-quality, human- annotated samples);
(iii) training with synergistic blend of the SuperRS- VQQ dataset and the HighRS-VQA dataset (comprising 12.228 high-quality, numan-annotated and 69,139 synthetically gen- erated samples).
As shown in Tab. 13, although VRSBench contains approximately ten times more samples than VQQA training solely on SuperRS-VQA yields significantly better performance (49.4 vs. 34.8). This is likely attributed to the substantial resolution gap: VRSBench averages 512x512 pixels, while SuPerRS-VQA averages 8.376x8.377 and also reflects the higher annotation quality of SuperRS-VQA. A further accuracy boost to 51.4 was observed when incorporating the HighRS- VQA dataset, validating its complementary value and effectiveness.
A.5 More implement details of Pilot Experiments.
This section provides more implementation details of the experiments in Section 4.1-4.2.
Multi-level Semantic Parsing Framework in Sec.4.1.
The framework separates the description from the evaluation content and processes it in five steps:
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(1) Urban Semantic Recognition, which detects terms like "city" or "urban" and reduces the back- ground probability if identified;
(2) Man-made Structure Analysis, which evaluates semantic relationships between terms like "building," "road," and negation words;
(3) Natural Element Classification, which calculates the weight of features like "water" and "for- est";
(4) Semantic Categorization, which classifies image regions as "Dense," "Sparse," or "None";
(5) Probability Gradient Quantification, which assigns values based on semantic strength, such as 20% for urban semantics, 30% for composite man-made structures, and up to 95% for pure natural features.
logit lens Technique in Sec.4.1. For each hidden state hj at position in layer 1, we project it into a probability space of tokenizer vocabulary and select the character with the highest logit. The original paper notes that visual-language models can optimize the alignment between visual and language modalities, enabling visual token representations in deeper layers to decode into language tokens that reflect object semantics. Fig. 9 shows additional examples using the logit lens technique to analyze the semantic content of visual tokens.
"wlamn' x
'sp'
"rmm"
Tr'
"and"
"har"
'of"
Water
V
"sea
"oceal
"perted
Backgroud Sample
(b) Backgroud Sample2
Figure 9: Visual tokens and their semantics detected by the logit lens technique.
Reliability Image Prepare in Sec.4.2. The images are sourced from XLRS-Bench, with two pre- processing steps applied to ensure reliability:
(1) High signal-to-noise sub-images: Following [60]. we apply target-centered cropping to increase the object coverage ratio relative the cropped image size, thereby enhancing target visibility.
(2) Hallucination control: Models may infer the presence of objects from context even when visual removed. To counteract this, generate control version with the target masked by noise. We keep only those samples where the model cor- rectly identifies the object in the original but fails in the occluded version, ensuring that recognition relies on visual evidence rather than context.
versions of each image: the original and
Experimental details in Sec.4.2. Token ablation is performed via substitution. Specifically, token positions corresponding to target objects are identified using ELRS-Bencee grounding annotations. These tokens are then replaced with fixed average embedding, computed the mean of all visual tokens across the XLRS- XLRS-Bench images used this experiment.
We use two methods to assess the impact of token ablation:
Generative Description: The model is prompted with "Describe the image within the bounding box" using both the original embeddings (F,)) and the ablated embeddings (E')). We compare whether the object appears in the generated descriptions. If it is mentioned with EA but not with E the ablated tokens are deemed crucial for recognizing the object.
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To ensure the response targets the annotated object, we include the bounding box in the prompt, given the frequent repetition similar objects remote sensing images.
2. Visual Question Answering (VQA): We used GPT-4o generate detailed questions for 400 manually selected remote sensing images, drawn from the XLRS-Bench subset used in this experiment. For example, "What located the edge of the large paved area, just south of the cluster of buildings with flat roofs, and adjacent to the narrow green space between the two structures?" All questions were reviewed for clarity and avoid explicitly naming the target (e.g., "boat"), The models answer is prefixed with "It is a " and we compare its responses before and after ablation.
A.6 Cases of Ve-SL
In this section, we present representative examples from XIRSS- Fig. 10. Each exam- ple compares the closed-source Gemini model with the latest open-source models: InterVL--3 and Qwen2.5-VEL across various VQA task formats. Extracting and interpreting objects from large-scale remote sensing imagery remains highly challenging; nonetheless, GeoLLaVA-8K exhibits strong performance in object recognition, localization, and counting under complex, real-world conditions.
27

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How many tennis courts are there within the red circle?
(A) 1
(8)
(C) 2
(D) 7
Gemini 2.0 Flosh:
(A)
InternVL3-88:
(A)
Qwen2.5-VL-725.
(C) X
GeoLLaVA-8K(Ounk
(A)
(a) Case of regional counting task.
Identify the object category within the given bounding
satellite Image resolution: 10000 10000. Bounding box: [7472, 2102, 7494, 2128].
image.
(A) The Gray Parking Garage
(3) The Blue Swimming Pool
(c) Blue Roof
(D) The Green Swimming Pool
Gemini Flash:
(B)
InternVL3-8B
(C)
X
Qwen225-VLL-7B:
(C)
X
GeoLLaVA-BK((ur))
(B)
(b) Case of object classification task.
How many boats are there entire picture?
(A)
(C) 12
(D) 15
3
Gemini Flash:
(A)
X
IntermVLL-8B:
(A)
X
Qwen2.5-VL-728
(A)
X
GeoLLaVA-8K(Our)
(B)
(c) Case of overall counting task.
Figure 10: Cases of Different Task on Various MLLMs (Part 1)
28

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How many there inside
the red cincle?
(A)
(B) 4
(C) 6
(D)
Gemini 2.0 Flash:
(A)
InternVLL-88.
(A)
Qwen2.5-VL-728:
(B)
GeoLLaVA-SK(Our)
(B)
(d) Case of regional counting task.
In the image, largest white vessel docked is located in which direction relative to the largest parking lot in the harbor?
(A) To the left left
(C) Bortom right corner
(D) Above
Gemini Flash:
(D)
InternVLL-8B.
(D)
X
Qwen2.5-VVL-72.
(D)
X
GeoLLaVAA-8((ur))
(B)
(e) Case of object spatial relationship task.
Figure 10: Cases of Different Task on Various MLLMs (Part 2)
29

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A.7 Datasheets
In this section, we document essential details about the proposed datasets and benchmarks following the NeurlPS Dataset and Benchmark guidelines and the template provided by Gebru et al. [61].
A.7.1 Motivation
The questions in this section are primarily intended encourage dataset creators to clearly articulate their reasons for creating the dataset and to promote transparency about funding interests. The latter may be particularly relevant for datasets created for research purposes.
1. " "For what purpose was the dataset created?"
A: Ultra-high-resolutionn (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to the key bottlenecks: limited availability of UHR training data. To address data scarcity, we in- troduce SuperRS-Ve VQA (avg. 8,376x8,376) and HighRS-VQA (avg. 2,000 1,912), the rrshesotresolution vision- vision-language datasets in RS to date, covering 22 real-world dialogue tasks.
2. "Who created the dataset (e.g, which team, research group) and on behalf of which entity?"
A: The dataset was created by the following authors:
Anonymous authors
3. " "Who funded the creation of the dataset?"
A: The dataset creation was funded by the affiliations of the authors involved in this work.
A.7.2 Composition
Most of the questions in this section intended provide dataset consumers with the information they need to make informed decisions about using the dataset for their chosen tasks. Some of the questions are designed elicit information about compliance with the EUs General Data Protection Regulation (GDPR) or comparable regulations in other jurisdictions. Questions that apply only to datasets that relate to people are grouped together at the end of the section. We recommend taking a broad interpretation of whether a dataset relates to people. For example, any dataset containing text that was written by people relates to people.
1. "What do the instances that comprise our datasets represent (e.g., documents, photos, peo- ple, countries))"
A: The dataset primarily consists of uitra-tigh-resooution remote sensing images captured by satellites, along with their corresponding textual annotations. All datasets utilized in SuPerRS-VQA and HighRS- VQA are publicly accessible and nonprofit.
2. " 'How many instances are there in total (of each type, if appropriate)?"
A: SuperRS-VQA and HigA includes 81,367 VQA pairs. Details could be found in the Table 2 in main text.
3. " "Doe the dataset contain all possible instances or is it sample (not necessarily random) of instances from a larger set?"
A: The images in Superks-VOR and HighRS- are sourced from existing detection
and segmentation datasets, but all textual annotations were independently created by us.
4. "Is there label or target associated with each instance?"
A: Yes, for these ultra- ultra-high-re resouutinn images, we have provided VQA pairs instances.
5. "Is any information missing, from individual instances?"
A: No, each individual instance is complete.
6. " Are relationships between individual instances made explicit (e.g.. users movie ratings, social network links)?"
A: Yes, the relationship between individual instances is explicit.
7. Are there recommended data splits (e.g.. training, development/validatton testing)?"
A: The dataset is designed to train the UHR RS MLLMs.
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8. "Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g.. websites, tweets, other datasets)?"
A: SuperRS-VQA and HighRS-VQA are self-contained and will be open-sourced on plat- forms like Hugging Face for easy use.
9. " DDoes the dataset contain data that might be considered confidential (e.g. data that pro- tected by legal privilege or by doctorpatient confidentiality, data that includes the content of individuals non-public communicatons))?
A: No, all data are clearly licensed.
10. " Does the dataset contain data that, if viewed directly, might be offensive, insulting, threat- ening, or might otherwise cause anxiety?"
A: No, SuperRS- - and SighRS-VQA do not contain any data with negative informa- tion.
A.7.3 Collection Process
In addition the goals outlined in the previous section, the questions in this section are designed to elicit information that may help researchers and practitioners create alternative datasets with similar characteristics. Again, questions that apply only datasets that relate to people are grouped together at the end of the section.
1. "How was the data associated with each instance acquired?"
A: The images in SoperRS-VQA and HighRS-VQA are sourced from existing detection and segmentation datasets. We enrich these altra-high-resolle images with manual an- notations, including 81,367 VQA pairs instances. Details are shown in the Section 3 in main text.
2. " Whha mechanisms or procedures were used to collect the data (e.g. hardware apparatuses sensors, manual human curation, software programs, softwrre APIs)?"
A: We employed professional annotation and quality control teams to complete the anno- tations for VQA tasks in SuperRS- VVQA. For MHR data, we developed a semi-automated annotation. Using specifce prompts and existing annotations (e.g., bounding boxes in RS detection datasets), we generated text via GPT-4o. We further adopted influence- based data selection pipeline improve the relevance our dataset to UHR downstream tasks and ensure its cultivation of reasoning capabilities for models fine-tuned on it.
3. "If the dataset is sample from larger set, what the sampling strategy (e.g., determin- istic, probabilistic with specific sampling probabilities)?"
A: Please refer to the details listed in the main text Section 3.
A.7.4 Preprocessing, Cleaning, and Labeling
The questions in this section are intended to provide dataset consumers with the information they need to determine whether the raw data has been processed in ways that are compatible with their chosen tasks. For example, text that has been converted into "bag-of-words words" is not suitable for tasks involving word order.
1. "Was any preprcesing/cteminnglbbii of the data done (e.g. discretization or bucket- ing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)?"
A: Yes. To minimize the redundancy image-text pairs, we deduplicate images within these datasets and remove overlaps with existing benchmark datasets like XLRS- Bench [14].
2. "Was the 'raw' data saved in addition to the prepmocesaallleennddhbbee data (e.g. to support unanticipated fature uses)?"
A: Yes, raw data accessible.
3. "Is the software that was used to preprocess/clenn/ubel the data available?"
A: Yes, the necessary software used to preprocess and clean the data is publicly available.
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A.7.5 Uses
The questions in this section are intended to encourage dataset creators to reflect tasks for which the dataset should and should not be used. By explicitly highlighting these tasks, dataset creators can help dataset consumers make informed decisions, thereby avoiding potential risks or harms.
1. Has the dataset been used for any tasks already?"
A: No.
2. "Is there repository that links any all papers systems that the dataset?"
A: Yes, we will provide such links in the GitHub and the Huggingface repository.
3. What (other) lasks could the dataset be used for?"
A: SuperRS-VQA and HighRS-VQA provide extensive annotations for VQA tasks. could be used for training the MLLMs.
4. "Is there anything about the composition of the dataset the way was collected and prepocessedcclemeedibeeeel that might impact future uses?"
A: No.
5. "re there tasks for which the dataset should not be used?"
A: N/A.
A.7.6
Distribution
Dataset creators should provide answers to these questions prior to distributing the dataset either internally within the entity on behalf of which the dataset was created or externally to third parties.
"Will the dataset be distributed to third parties outside of the entity (c.g., company, institu- tion, organization) behalf of which the dataset was created?"
A: The datasets will be made publicly accessible to the research community.
2. " "How will the dataset distributed (e.g., turball on website, API, GilHub)?"
A: We will provide uperRS-VQL and HighRS-VQA in the GitHub and the Huggingface repository.
3. "When will the dataset be distributed?"
A: We will create a repository to release the data once the paper is officially published.
4. Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)?"
A: Yes, the dataset will be released under the Creative Commons Attribution-
DonCommercial-ShareAAle
4.0 International License.
5. " Haae any third parties imposed IP-based or other restrictions the data associated with the instances?"
A: No.
6. " Do any export controls or other regulatory restrictions apply to the dataset or to individual instances?"
A: No.
A.7.7 Maintenance
As with the questions in the previous section, dataset creators should provide answers to these ques- tions prior to distributing the dataset. The questions in this section intended to encourage dataset creators to plan for dataset maintenance and communicate this plan to dataset consumers.
1. " Who will be supporingghotinggnniitininnn the dataset?"
A: The authors of this work serve to support, host, and maintain the datasets.
2. "How can the owner/curator/manager of the dataset be contacted (e.g., email address)?"
A: They can be contacted via the email addresses listed on the paper webpage.
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3. "Is there erratum?"
A: There is explicit erratum; updates and known ertors will be specified in future sions.
4. Will the dataset be updated (e.g., to correct labeling eroos, add new instances, delete instances)?"
A: Future updates (if any) will be posted the dataset website.
5. "Will older versions of the dataset continue to be supportelhooseddniinaanddd A:
Yes. This initial release will be updated the future, with older versions replaced as new updates posted.
6. "If others for them to do so?"
to extend/aaggment/uild on/contribute to the dataset, is there mechanism
A: Yes, we will provide detailed instructions for future extensions.
A.8 Limitation and Potential Societal Impact
In this section, we discuss the limitations and potential societal impact of this work.
A.8.1
Potential Limitations (Ge8LLaVA-8K)
Scope of Sensors: GeoLLaVA-88 is tuned for satellite visiblelight imagery; its perfor- mance on SAR, multispectral or street-level data is unverified, limiting cross-domain gen- eralisability.
Model Scale: We used only basic 7B model, while commercial models like Qwen and Intern have reached 72B. We also aim to scale up model parameters for improved per- formance.
A.8.2 Potential Positive Societal Impacts LaV-I.L
Fine-grained Environmental Intelligence: Enables rear-real-time QA over 8K imagery for detecting illegal logging, coastal crosion or oil spills, supporting evidence-based policy and SDG monitoring.
Disaster Response Acceleration: Rapid localisation of collapsed bridges, blocked roads or inundated zones can sharpen rescue logistics and reduce casualty rates.
A.8.3 Potential Negative Societal Impacts (GeoLLaVA-8Kk
Surveillance & Dual-Use Risk: High- precision localisation of vehicles assets lowers the barrier for persistent monitoring and autonomous targeting.
Over-reliance on Automated QA: Persuasive textual answers may be taken at face value; misclassification of disaster extent land-use could misdirect resources.
A.8.4 Potential Limitations (SuperRS-VQA & HighRS-VQA)
The datasets dominated by RGB optical satellites; absence of SAR or hyperspectral samples constrains multimodal fusion research.
Sensor Homogeneity:
Annotation Consistency: Crowdsourced VQA answers could contain subtle errors or re- gional terminology variance, introducing noise in supervision signals.
A.8.5 Potential Positive Societal Impacts (SuperRS-VQL VQQA & HQg)
Open Benchmark Catalyst: By releasing the largest-image-size RS VQA corpora (up to 8,376C88,376), the datasets establish transparent yard-stick for future UHR-aware algo- rithms.
Policy- Rellvv-nl Insights: Rich dialogue tasks (22 subtasks) mirror real-world queriese.g. Count illegal fishsarmsprooiiiigg sandbox for developing decision-support tools that aid sustainable development.
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A.8.6 Potential Negative Societal Impacts (SuperRS-VQA & HighRS-VQA)
Privacy Concerns: 153em GSD tiles reveal rooftop activity; malicious actors could deanonymise locations or monitor private property.
Environmental Footprint Training: Fine-tuning large models on tens of thousands of 8K images consumes significant energy; if replicated widely without checkpoint reuse, aggregate carbon emissions risc.
34


Reviews:
[{"review_id": "yElipUEweT", "reviewer": "Reviewer_43pb", "summary": "This paper introduces GeoLLaVA-8K, a remote sensing (RS)-specific multimodal large language model designed to address two key challenges in ultra-high-resolution (UHR) RS imagery: scarce training data and token explosion. The authors first construct SuperRS-VQA (8K resolution) and HighRS-VQA (2K resolution), two datasets covering 22 real-world RS tasks, providing high-resolution data for model training. Observing that RS images feature redundant background tokens and sparse target tokens, they propose Background Token Pruning to remove redundant background information and Anchored Token Selection to preserve target-related tokens, reducing computational overhead while maintaining key semantics. Experiments show that GeoLLaVA-8K achieves state-of-the-art (SOTA) performance on the XLRS-Bench benchmark, outperforming all existing models in 8K-resolution RS tasks.", "questions": "1. Could the authors supplement detailed ablation results in the main text to clarify the individual contributions of increasing high-resolution data, Background Token Pruning, and Anchored Token Selection strategies? For example, how does removing each strategy alone affect model accuracy on XLRS-Bench?\n2. Could the authors provide experimental data on different token compression ratios to demonstrate how compression ratios influence both computational efficiency and semantic retention? Are there optimal ratios for different RS tasks?\n3. Was a control experiment conducted to compare models trained solely with high-resolution datasets versus those using the token compression strategies? Please clarify whether performance improvements primarily derive from data resolution or strategy efficiency.\n4. Could the authors analyze specific failure cases where GeoLLaVA-8K underperforms baseline models (e.g., LLaVA-Next in certain sub-tasks) to identify whether token compression causes information loss in scenarios with dense small targets or complex backgrounds?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths**:\n1. High-Resolution Datasets：SuperRS-VQA and HighRS-VQA fill the gap in UHR RS training data, offering 10× higher resolution than previous datasets. Data quality is enhanced via influence function-based selection, aligning with real-world RS scenarios.\n2. Efficient Token Compression：Background pruning reduces 50% of redundant background tokens, while Anchored Token Selection leverages attention mechanisms to retain target-relevant tokens, enabling 8K processing without memory overflow and improving computational efficiency.\n3. Superior Performance：With only 7B parameters, GeoLLaVA-8K outperforms 72B closed-source models, excelling in complex tasks like object counting and route planning, which validates the effectiveness of domain-adapted modeling.\n4. Clear Research Logic：The paper presents a coherent research pipeline, from problem identification to dataset construction and strategy design, ensuring accessibility for readers.\n5. Open-Source Reproducibility: By committing to release datasets (SuperRS-VQA/HighRS-VQA) and code, the research provides a standardized framework for the RS community. This fosters reproducibility, with 7B-parameter model training procedures detailed sufficiently for replication using 16 A100 GPUs.\n\n**Weaknesses:**\n1. Incomplete Experimental Validation：While supplementary materials include ablation studies, the main text lacks detailed descriptions of experimental procedures, especially insufficient ablation analyses that hinder the completeness of findings.\n2. Experiments on token compression ratios are insufficient, making it difficult to quantify their impact on overall performance.\n3. Lack of comparisons between models trained solely with high-resolution data versus those using both data and compression strategies, limiting clarity on strategy contributions.\n4. In certain scenarios (e.g., specific sub-tasks in Table 4), GeoLLaVA-8K underperforms baseline models like LLaVA-Next, suggesting potential negative impacts of token compression strategies in niche contexts.", "was_revised": true, "final_justification": "After carefully reviewing the response, The authors' explanations have further strengthened the paper's arguments and solidified my confidence in the contribution and rigor of this work. Therefore, I am happy to maintain my initial positive score.", "rebuttal": "## Q1:More ablation results\nWe fully agree with your suggestion. The ablation studies in appendix, along with the additional experiments and deeper analysis completed during the rebuttal, should be incorporated into the main text. As your recommended, we evaluated the baseline model LLaVA-Next on our high-resolution datasets (SuperRS-VQA and HighRS-VQA).\n\n**Experimental Setup:** We followed LLaVA-Next’s original settings and used the same hyperparameters as in the GeoLLaVA-8K experiments (epoch=1, lr=5e-6, Vision\\_tower\\_lr=1e-6, warmup\\_ratio=0.1, etc.). 'Dataset' means SuperRS-VQA and HighRS-VQA, 'BTP' means the background token pruning method, 'ATS' means anchor token selection method.\n|Subtasks|OC|RC|OLUC|RLUC|OCC|OCL|OMS|OSR|AD|ECR|RP|RCCD|CCR|Avg.|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|LLaVA-Next-2k (**Baseline**)|28.3|47.0|3.0|62.0|42.0|34.1|66.7|27.2|73.0|75.0|34.0|  48.3|40.0|44.7|\n|Baseline+Dataset|25.9|41.0|50.0|68.0|40.4|28.5|64.9|33.4|58.0|77.0| 57.0|45.0|50.0|49.1|\n|Baseline+Dataset+BTP ||||||||||||||OOM|\n|Baseline+Dataset+BTP+ATS(**GeoLLaVA-8k**)|26.7|38.0|49.0|69.0|41.6|31.6|65.0|35.0|67.0|78.0|66.0|50.0|52.0|51.5|\n\nThe results show that while training the baseline model on HighRS-VQA and SuperRS-VQA improves performance, further gains are achieved by adding our BTP and ATS strategies. Most subtasks benefit from our datasets and methods, though performance declines are observed in Overall Counting, Regional Counting, Object Color, Object Motion State, and Anomaly Detection.These drops stem from the datasets themselves, which lack sufficient or relevant samples for these tasks. Our method helps alleviate this issue. For example, the Regional Counting task in XLRS-Bench requires counting small objects within red circles—a scenario rarely found in our SFT data. Addressing this requires generating samples that improve the model’s ability to follow such instructions to detect red circles and better capture small-object features.\n\n**The background token pruning and anchor token selection strategies are strongly coupled.** The first strategy alone causes OOM issues, prompting the need for the second. As an adaptive, iterative method with learnable parameters, the background token pruning struggles to handle numerous background tokens in early training, improving only in later training stages. Therefore, we will conduct additional ablation studies to examine their joint performance under varying compression ratios in the following responce. \n## Q2:Experiments on different token compression ratios\nThe appendix includes an ablation study on compression ratios, but only at three levels. We extended the study to five compression ratios, reporting both computational efficiency and semantic preservation (accuracy). We also detail subtask-level performance across these ratios with comprehensive discussion. \n|Subtasks|OC|RC|OLUC|RLUC|OCC|OCL|OMS|OSR|AD|ECR|RP|RCCD|CCR|Avg.|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|16x(OOM)|\n|24x|26.7|38.0|49.0|69.0|41.6|31.6|65.0|35.0|67.0|78.0|66.0|50.0|52.0|51.5|\n|32x|28.3|39.0|46.0|65.5|42.3|34.1|65.0|35.6|64.0|74.0|63.0|46.7|50.0|50.3|\n|48x|30.0|41.0|46.0|65.0|44.3|31.3|65.0|36.2|64.0|75.0|60.0|44.0|50.0|50.1|\n|96x|43.3|44.0|35.0|62.0|42.1|23.9|65.0|37.2|63.0|74.0|61.0|43.3|52.0|49.7|\n\nThe table presents compression ratios applied consistently during both training and testing. Overall, performance declines with higher compression, with the best result—54.5 accuracy—achieved at 24×. Interestingly, some subtasks improve with higher compression, supporting your commnets *“there are optimal ratios for different RS tasks”*.  Notably, OC and RC see substantial gains under 96× compression, significantly outperforming the baseline (LLaVA-Next). As GeoLLaVA-8K is a unified model, we use a single global compression ratio (24x) for evaluation. We encourage future work to explore task-specific compression strategies, a promising direction for impactful standalone research.\nWe also provide inference speed and FLOPs for each compression ratio. We employ the tool calflops and report Tera FLOPs (TFLOPs) of the LLM side to exclude the computation incurred by the vision encoder. The results is tested using a image of a resolution of 10,000 $\\times$ 10,000 which results in 332k image patches for our 8k model. \n|Compression|Tokens per Grid|Visual Token Volume|TFLOPs of LLM|Inference Speed(second per image)|Avg.|\n|-|-|-|-|-|-|\n|16x(OOM)|\n|24x|24|14.0k|198.06|2.17|51.5|\n|32x|18|10.5k|149.08|2.03|50.3|\n|48x|12|7.1k|100.11|1.69|50.1|\n|96x|6|3.6k|51.13|1.46|49.7|\n\nGeoLLaVA-8K supports resolutions up to 8K×8K, generating a large number of visual tokens. With a 24× compression ratio, about 14K tokens are produced, resulting in higher FLOPs and slower inference. Increasing the ratio to 96× significantly reduces tokens, boosts TFLOPs and inference speed, while maintaining strong performance—49.7 accuracy, outperforming Qwen2.5VL-7B's 47.4. As our focus is model performance, we adopted the 24× setting. In future work, we plan to optimize inference speed and better balance performance and efficiency.\n## Q3: Whether performance improvements primarily derive from data resolution or strategy efficiency.\nYour feedback is both constructive and timely. As noted in our response to your first question, we conducted separate experiments on the baseline model using the HighRS-VQA and SuperRS-VQA. The results clearly validate the effectiveness of our datasets and token compression strategies.\n## Q4: Failure cases to identify whether token compression causes information loss in scenarios with dense small targets?\nUnfortunately, the latest NeurIPS rebuttal policy does not allow image submissions. Therefore, We address this issue from two perspectives, backed by extensive experiments.\n\n**(1) GeoLLaVA-8K does not cause significant loss of small-object information.**\n\nWe designed a new experiment to address your concern regarding whether token compression leads to the loss of dense small-object information. We extracted samples that meet the definition of small objects from relevant subtasks in XLRS-Bench to create a new small-object subset of the benchmark. Specially, we selected six small-object-related tasks from XLRS-Bench—**object classification, object color, motion state, global counting, regional counting, and spatial relation reasoning**. After manual verification, we curated a dedicated small-object subset, which will be open-sourced to support related research.\n* **Small Object Criteria:** COCO dataset defines small objects by absolute pixel area (<32² pixels), which is less suitable for UHR RS images. Given COCO's typical image size (400–650 pixels), small objects occupy roughly 2.5%–5% of the image area. Therefore, we adopt a relative area-based classification: Extremely Small: <0.1% of image area, Very Small: 0.1%–2.0%, Normal Small: 2.0%–5.0%, Large: ≥5.0%.\n* **For object classification, object color, and motion state tasks**—which include bounding boxes—we calculated the pixel area of each object. We identified 1,450 samples where objects occupy less than 5% of the image. \n\n* **For counting and spatial relation tasks** without bounding boxes, we manually annotated targets and calculated their area ratios. Samples with any object exceeding 5% of the image area were excluded, yielding 40 test samples per subtask. Each sample contains only small objects, categorized by the smallest object's area ratio into Very Small(<2.0%) and Normal Small(2.0%–5.0%).\n\n\n|Task|Extremely Small (<0.1%)|Very Small (0.1%-2.0%)|Normal Small (2.0%-5.0%)|Total|\n|-|-|-|-|-|\n|Object Classification|531|166|3|700|\n|Object Color|597|101|2|700|\n|Object Motion State|38|12|0|50|\n|Regional Counting|-|37|3|40|\n|Overall Counting|-|38|2|40|\n|Spatial Relationship|-|38|2|40|\n|Overall|1,166|392|12|1,570|\n\n* **Results**: GeoLLaVA-8k outperforms the baseline across all six tasks in the newly constructed small-object test set, raising overall accuracy from 34.05% to 40.25% (+6.20%). The most significant gain is in spatial relation reasoning (+15%), followed by global counting (+10%) and motion state (+8%). Color recognition shows the lowest accuracy (34.43%), highlighting the difficulty of extracting color features from small objects. These results suggest that high-level cognitive reasoning tasks hold greater potential for improvement than basic perceptual ones.\n\n|Dataset|Object Classification|Object Color|Object Motion State|Regional Counting|Overall Counting|Object Spatial Relationship|Overall|\n|-|-|-|-|-|-|-|-|\n|LLaVa-Next|38.6|29.4|62.0|35.0|22.5|20.0|34.1|\n|Geollava-8k|45.3|34.4|70.0|40.0|32.5|35.0|40.3|\n\nOverall, GeoLLaVA-8K significantly outperforms the baseline LLaVA-Next on small-object tasks, highlighting the substantial benefits of higher resolution(8kx8k). However, as noted in your comment on “*information loss in scenarios with dense small targets*,” GeoLLaVA-8K still struggles—achieving only \\~34% accuracy on some tasks. This indicates limited capability in extracting features from dense small targets. Addressing this limitation should be a key focus for future research. \n\n**(2) Failure Cases**\n\nIn a regional counting task, GeoLLaVA-8K failed to accurately count ships within a red-circled of small harbor. Although 53 ships were present within harbor and 38 within the red circle, the model misinterpreted the instruction, counted the entire harbor, and returned an incorrect result. These errors likely stem from a lack of relevant training samples with red circles.  Addressing this will require generating more targeted samples to enhance the model's ability to follow spatial cues and detect small-object features. While GeoLLaVA-8K generally outperforms the baseline, occasional failure cases—as you pointed out—highlight areas for future improvement.\n\n**All of your suggestions are truly constructive, and our team unanimously agrees that they will greatly improve the quality of our work. We sincerely appreciate again for your valuable guidance.**"}, {"review_id": "mfh8rYvDRi", "reviewer": "Reviewer_FLPi", "summary": "This presents GeoLLaVA-8K, a multimodal LLM designed for high-resolution remote sensing tasks. The authors introduce SuperRS-VQA (avg. 8,376×8,376) and HighRS-VQA (avg. 2,000×1,912), the highest-resolution vision-language datasets in RS to date, covering 22 real-world 7 dialogue tasks. The authors also propose two strategies: Background Token Pruning and Anchored Token Selection, to reduce the memory footprint caused by the high resolution while preserving key semantics. Ablation studies demonstrate the effectiveness of the design. Experiments show that GeoLLaVA-8K achieves the best performance compared to closed-source or open-source models.", "questions": "- Similar to the weakness, it would be helpful to show a comparison with other datasets.\n- What is the difference between the tasks that GeoLLaVA-8K performs better or worse, and what could be the potential reason?", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n\n- The datasets presented in the paper have a large number of samples and cover diverse tasks, which can be very helpful for future research.\n- The proposed designs for the model are well-motivated by the features of RS data (e.g., a large fraction of background regions) and lead to better performance. Background Token Pruning and Anchored Token Selection could also be easy to implement.\n- The paper writing is clear and easy to understand.\n\n**Weaknesses:**\n\n- The comparison with other models is only conducted on the XLRS-Bench, while preliminary experiments for the designs are also performed on the same benchmark. More datasets for evaluation are needed to show the generalizability of the method.\n- Only certain types of tasks show performance benefit, like RP, CCR, and OLUC. But on tasks like RC, AD, and OC, the performance is actually not superior.", "was_revised": true, "final_justification": "Thank you, authors, for addressing all my questions. I will maintain my already positive score.", "rebuttal": "## Q1: Show comparison with other datasets.\nWe agree that more benchmark datasets should be included. At the time of submission to NeurlPS'25, UHR remote sensing MLLM benchmarks were scarce, with XLRS-Bench being the only open-source, formally published option. \n\nDuring the rebuttal phase, and following your suggestion, we identified LRS-VQA—a newly accepted ICCV 2025 benchmark—well-suited for evaluating MLLMs on ultra-high-resolution remote sensing tasks. However, LRS-VQA only offers open-ended QA formats, which are incompatible with standard evaluation platforms like VLMEval.\n\nTo address this, we created a multiple-choice version of the LRS-VQA dataset and shared it with the LRS-VQA authors and VLMEval platform to support broader community evaluation. Our processing pipeline for LRS-VQA is as follows:\n\n1. **Plausible Distractor Generation:** We employed a large language model (GPT-4o) to generate three semantically plausible distractors for each question. The prompt explicitly instructed the model to ensure that the distractors:\n   - Are relevant to the question context,\n   - Belong to the same semantic category as the correct answer (e.g., vehicle types, object status),\n   - Do not overlap with the correct answer.\n2. **Binary Question Handling:** For binary or contrastive questions (e.g., “rural or urban”, “yes or no”), we automatically restricted the number of answer choices to two, ensuring alignment with the question format and avoiding unnecessary ambiguity.\n3. **Strict Format Enforcement and Post-validation:** We enforced a strict JSON structure during generation, requiring each output to contain:\n   - The original question,\n   - Exactly two or four answer choices labeled with keys \"A\", \"B\", (and optionally \"C\", \"D\"),\n   - A valid `answer` key pointing to the correct choice.\n      All generated outputs were further post-processed to validate their structural consistency and ensure that the correct answer was included and accurately labeled.\n\nAs a result, we successfully generated 7,145 MCQ-style QA pairs out of the 7,333 original questions. This allowed us to perform additional quantitative evaluations using a multiple-choice format that complements the original open-ended benchmark.\n\nWe evaluated LLaVA-Next and our GeoLLaVA-8K on LRS-VQA, with **GeoLLaVA-8K achieving 56.28% accuracy—1.21% higher than the LLaVA-Next baseline which achieving 55.07%**—demonstrating our generalization. Looking forward, our data generation pipeline which was used for generating HighRS-VQA can be readily adapted to augment LRS-VQA, further boosting GeoLLaVA-8K’s performance. We will open-source the data generation pipeline to support the community in generating high-quality training data for ultra-high-resolution remote sensing tasks.\n\n\n## Q2:What is the difference between the tasks that GeoLLaVA-8K performs better or worse, and what could be the potential reason?\n\nDue to space constraints, the main text lacks detailed subtask analysis. Therefore, we present the following analysis:\n\n|Subtasks|OC|RC|OLUC|RLUC|OCC|OCL|OMS|OSR|AD|ECR|RP|RCCD|CCR|Avg.|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|LLaVA-Next-2k (baseline)|28.3|47.0|3.0|62.0|42.0|34.1|66.7|27.2|73.0|75.0|34.0|  48.3|40.0|44.7|\n|Baseline+SuperRS-VQA and HighRS-VQA|25.9|41.0|50.0|68.0|40.4|28.5|64.9|33.4|58.0|77.0| 57.0|45.0|50.0|49.1|\n|Baseline+SuperRS-VQA and HighRS-VQA + two token pruning strategies|26.7|38.0|49.0|69.0|41.6|31.6|65.0|35.0|67.0|78.0|66.0|50.0|52.0|51.5|\n\nMost subtasks benefit from our datasets and methods, though performance declines are observed in Overall Counting, Regional Counting, Object Color, Object Motion State, and Anomaly Detection，comparing with baseline. These drops stem from the datasets, which lack sufficient or relevant samples for these subtasks. Our proposed method helps alleviate this issue. For example, the Regional Counting task in XLRS-Bench requires counting small objects within red circles—a scenario rarely found in our SFT data. Addressing this requires generating samples that improve the model’s ability to follow such instructions to detect red circles and better capture small-object features.\n\nWhile extending the GeoLLaVA-8k to five compression ratios, we discovered an intriguing finding: **Several underperforming subtasks (OC and RC) in GeoLLaVA-8K showed significant improvement with higher compression ratios.**\n \n|Subtasks|OC|RC|OLUC|RLUC|OCC|OCL|OMS|OSR|AD|ECR|RP|RCCD|CCR|Avg.|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|LLaVA-Next-2k (baseline)|28.3|47.0|3.0|62.0|42.0|34.1|66.7|27.2|73.0|75.0|34.0| 48.3|40.0|44.7|\n|24x|26.7|38.0|49.0|69.0|41.6|31.6|65.0|35.0|67.0|78.0|66.0|50.0|52.0|51.5|\n|32x|28.3|39.0|46.0|65.5|42.3|34.1|65.0|35.6|64.0|74.0|63.0|46.7|50.0|50.3|\n|48x|30.0|41.0|46.0|65.0|44.3|31.3|65.0|36.2|64.0|75.0|60.0|44.0|50.0|50.1|\n|96x|43.3|44.0|35.0|62.0|42.1|23.9|65.0|37.2|63.0|74.0|61.0|43.3|52.0|49.7|\n\nThe table shows that compression ratios were applied consistently during training and testing. Overall performance declines with higher compression, with the best accuracy—54.5—achieved at 24×, the setting used in GeoLLaVA-8K. Interestingly, some subtasks improve with increased compression. We believe different RS tasks have distinct optimal compression levels—OC, RC, OCC, OCL, and OSR each exhibit unique preferences. Notably, OC and RC achieve substantial gains at 96× compression, outperforming the baseline (LLaVA-Next). This suggests that **downstream tasks may benefit from tailored compression ratio**. For instance, in OC and RC, higher compression may remove redundant information, helping the model focus more effectively on targets.\nAs GeoLLaVA-8K is a unified model, we use a single global compression ratio (24×) for evaluation. These results will be included in future versions. We encourage future research to explore task-specific compression strategies, a promising direction for impactful standalone work.\n\n**We sincerely thank you again for your constructive feedback. If you have any further questions or suggestions, we would be more than happy to address them.**"}, {"review_id": "XiTeIUDaRN", "reviewer": "Reviewer_HCHc", "summary": "This paper addresses the key challenges of applying Multimodal Large Language Models (MLLMs) to ultra-high-resolution (UHR) remote sensing imagery. The authors identify two primary bottlenecks: the scarcity of high-resolution training data and the \"token explosion\" problem, where the large image dimensions lead to computationally prohibitive sequences of visual tokens.\nTo tackle these challenges, the paper makes three main contributions:\n* Data: It introduces two new, large-scale VQA datasets for the RS domain: SuperRS-VQA, a manually annotated dataset with UHR images (avg. 8K resolution), and HighRS-VQA, a larger, semi-automatically generated dataset with medium-to-high resolution images (avg. 2K resolution).\n* Method: It proposes a novel two-stage token management strategy to handle UHR inputs efficiently. This involves (i) background token pruning, which uses semantic clustering to merge and remove redundant background tokens (e.g., ocean, forest), and (ii) anchored token selection, which leverages attention from the ViT's [CLS] token to retain the most semantically critical foreground tokens.\n* Model: It develops GeoLLaVA-8K, the a remote sensing focused MLLM capable of handling 8K resolution inputs. By integrating the proposed token reduction techniques and fine-tuning on the new datasets, GeoLLaVA-8K achieves new state-of-the-art results on the challenging XLRS-Bench benchmark, outperforming strong open and closed-source models.", "questions": "* This work is motivated by efficiency, but the efficiency of the method itself is not measured. Could the authors provide a brief analysis of the computational overhead of your method? A small clarification here would make the practical benefits clearer and provide a more complete picture of the practical trade-offs involved.\n* The use of [CLS] attention for selecting important tokens is a key design choice. Could you enhance the appendix with a small qualitative analysis on this point? For example, select 1-2 examples from the XLRS-Bench where the question involves reasoning about multiple small objects or their spatial relationship. Please visualize the [CLS] attention map and the final selected tokens. Does the attention successfully capture all task-relevant objects, or does it sometimes focus on a single, most prominent one?", "limitations": "The main paper includes details about the limitations of this work. The authors separately discuss the limitations and impacts for the model (GeoLLaVA-8K) and the datasets (SuperRS-VQA & HighRS-VQA), covering important angles like sensor scope.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n* The paper targets a highly significant and difficult problem at the frontier of MLLM capabilities. Scaling models to UHR imagery is a critical next step for many real-world applications, particularly in remote sensing where fine-grained detail is paramount. The work is timely and the problem is very well-motivated.\n* The creation and release of SuperRS-VQA and HighRS-VQA are substantial contributions in their own right. Addressing the data scarcity bottleneck with two high-quality, high-resolution datasets will undoubtedly be of great value to the research community. The methodology for creating HighRS-VQA, including the use of an influence-based data selection pipeline (LESS), is sophisticated and demonstrates a commitment to data quality.\n* The proposed token reduction strategy is elegantly motivated by a series of pilot studies (Sections 4.1, 4.2) that clearly demonstrate the redundancy of background tokens and the importance of sparse object tokens in RS imagery. The two-step approach of first pruning the background and then selecting the foreground is technically sound.\n* The experimental evaluation is comprehensive and convincing. GeoLLaVA-8K's performance on the difficult XLRS-Bench is impressive, setting a new state-of-the-art and outperforming even much larger models like Qwen2.5-VL-72B and proprietary APIs like GPT-4o. \n* The paper is well-written, clearly structured, and easy to follow. The figures, particularly Figure 1 (overview), Figure 5 (motivating token pruning), and Figure 7 (method schematic), are excellent aids for understanding the paper's core concepts and contributions.\n\nWeaknesses:\n* The \"Background Token Pruning\" assumes that backgrounds (like forests, water) are semantically homogeneous and can be easily clustered and removed. This might fail in complex, heterogeneous scenes (e.g., suburban areas, sparse agriculture) where the distinction between \"background\" and \"foreground\" is blurry, potentially causing important contextual information to be discarded. Moreover, the \"Anchored Token Selection\" assumes that the [CLS] token's attention is the optimal proxy for semantic importance for all VQA tasks. The [CLS] token is trained for global image classification. For reasoning tasks that depend on the relationship between multiple small but critical objects, the [CLS] token might focus on a single large, prominent object, causing the smaller but contextually vital objects to be pruned. This could be a significant limitation for complex reasoning.\n* The method is motivated by reducing the computational burden of UHR images. While it successfully reduces the number of visual tokens fed to the LLM, it introduces a non-trivial pre-processing step involving iterative clustering and attention map computation. The paper provides no analysis of the computational overhead (e.g., latency, FLOPs) of this method itself.", "was_revised": true, "final_justification": "I maintain my strong recommendation for acceptance (Rating: 5). After reviewing the authors' detailed rebuttal and considering the points raised by the other reviewers, my confidence in the quality and impact of this work has been further solidified. The paper's core strengths include: (1) tackling the critical problem of scaling MLLMs to UHR remote sensing imagery, (2) the contribution of two new high-resolution datasets (SuperRS-VQA, HighRS-VQA), and (3) the token management strategy. My primary concerns, shared in my initial review, included (1) the lack of analysis on the computational overhead of the proposed method and (2) the potential for the [CLS] token-based attention to fail in scenarios with multiple small objects. Both of my concerns were thoroughly addressed by the authors. All in all, i believe the extensive experiments added during the rebuttal have substantially strengthened the paper. I am confident this paper will be a valuable and impactful contribution to the community and strongly advocate for its acceptance.", "rebuttal": "## Q1: This work is motivated by efficiency, but the efficiency of the method itself is not measured. Could the authors provide a brief analysis of the computational overhead of your method? A small clarification here would make the practical benefits clearer and provide a more complete picture of the practical trade-offs involved.\n\nThanks for your constructive comments. We fully agree that it is necessary to analyse the efficiency. Therefore, we provide inference speed and FLOPs for each compression ratio. We employ the tool calflops and report Tera FLOPs (TFLOPs). We report FLOPs of the LLM side to exclude the computation incurred by the vision encoder, which in our model, accounts for more than a half of the computation. The results of Inference Speed are tested using a image of a resolution of 10,000 $\\times$ 10,000 which results in 332k image patches for our 8k model. \n|Compression|Tokens per Grid|Visual Token Volume|TFLOPs of LLM|Inference Speed(second per image)|Avg.|\n|----|---------------|--------------|------|-------|----|\n|16x(OOM)|\n|24x|24|14.0k|198.06|2.17|51.5|\n|32x|18|10.5k|149.08|2.03|50.3|\n|48x|12|7.1k|100.11|1.69|50.1|\n|96x|6|3.6k|51.13|1.46|49.7|\n\nThe table presents compression ratios applied consistently during both training and testing—for instance, a 24× ratio is used throughout training and evaluation on XLRS-Bench. Overall, performance declines with higher compression, with the best result—54.5 accuracy—achieved at 24×, the setting adopted by GeoLLaVA-8K. GeoLLaVA-8K supports resolutions up to 8K × 8K, generating a large number of visual tokens. With a 24× compression ratio, about 14K tokens are produced, resulting in higher FLOPs and slower inference. Increasing the ratio to 96× significantly reduces tokens, boosts TFLOPs and inference speed, while maintaining strong performance—49.7 accuracy, outperforming Qwen2.5VL-7B's 47.4. As our focus is model performance, we adopted the 24× setting. In future work, we plan to optimize inference speed and better balance performance and efficiency.\n\n\n## Q2: The use of [CLS] attention for selecting important tokens is a key design choice. Could you select 1-2 examples from the XLRS-Bench where the question involves reasoning about multiple small objects or their spatial relationship. Does the attention successfully capture all task-relevant objects, or does it sometimes focus on a single, most prominent one?\nWe offer comprehensive experiments on small targets to demenstrate that the \\[CLS] attention mechanism captures all task-relevant objects in GeoLLaVA-8k, it rarely focuses on the most prominent one. Future work could further improve \\[CLS] attention by incorporating fine-grained visual encoders from CLIP-based models such as FG-CLIP or FineCLIP.\n\nUnfortunately, the latest NeurIPS rebuttal policy does not allow image submissions. Therefore, we designed a new experiment to address your concern regarding whether token compression leads to the loss of dense small-object information. We extracted samples that meet the definition of small objects from relevant subtasks in XLRS-Bench to create a new small-object subset of the benchmark. We then evaluated both the baseline LLaVA-Next and our method on this subset. The detailed experimental procedure is outlined below.\n\nWe selected six small-object-related tasks from XLRS-Bench—**object classification, object color, motion state, global counting, regional counting, and spatial relation reasoning**. After manual verification, we curated a dedicated small-object subset, which will be open-sourced to support related research.\n* **Small Object Criteria:** COCO dataset defines small objects by absolute pixel area (<32² pixels), which is less suitable for high-resolution remote sensing images. Given COCO's typical image size (400–650 pixels), small objects occupy roughly 2.5%–5% of the image area. Inspired by this, we adopt a relative area-based classification: Extremely Small: <0.1% of image area, Very Small: 0.1%–2.0%, Normal Small: 2.0%–5.0%, Large: ≥5.0%.\n* **For object classification, object color, and motion state tasks**—which include bounding boxes—we calculated the pixel area of each object. We identified 1,450 samples where objects occupy less than 5% of the image. A detailed breakdown is provided in the table below.\n\n|Dataset|Extremely Small (<0.1%)|Very Small (0.1%-2.0%)|Normal Small (2.0%-5.0%)|Total|\n|-|-|-|-|-|\n|Object Classification|531|166|3|700|\n|Object Color|597|101|2|700|\n|Object Motion State|38|12|0|50|\n|Regional Counting|-|37|3|40|\n|Overall Counting|-|38|2|40|\n|Spatial Relationship|-|38|2|40|\n|Overall|1,166|392|12|1,570|\n\n* **For counting and spatial relation tasks** without bounding boxes, we manually annotated targets and calculated their area ratios. Samples with any object exceeding 5% of the image area were excluded, yielding 40 test samples per subtask. Each sample contains only small objects, categorized by the smallest object's area ratio into Very Small(<2.0%) and Normal Small(2.0%–5.0%).\n* **Experimental Results**: GeoLLaVA outperforms the baseline across all six tasks in the newly constructed small-object test set, raising overall accuracy from 34.05% to 40.25% (+6.20%). The most significant gain is in spatial relation reasoning (+15%), followed by global counting (+10%) and motion state (+8%). Color recognition shows the lowest accuracy (34.43%), highlighting the difficulty of extracting color features from small objects. These results suggest that high-level cognitive reasoning tasks hold greater potential for improvement than basic perceptual ones.\n\n|Dataset|Object Classification|Object Color|Object Motion State|Regional Counting|Overall Counting|Object Spatial Relationship|Overall Performance|\n|-|-|-|-|-|-|-|-|\n|LLaVa-Next|38.6|29.4|62.0|35.0|22.5|20.0|34.1|\n|Geollava-8k|45.3|34.4|70.0|40.0|32.5|35.0|40.3|\n\nOverall, GeoLLaVA-8K, trained on SuperRS-VQA and HighRS-VQA, significantly outperforms the baseline LLaVA-Next on small-object tasks, highlighting the substantial benefits of higher resolution(8kx8k). However, as noted in your comment on “information loss in scenarios with dense small targets,” GeoLLaVA-8K still struggles—achieving only \\~34% accuracy on some tasks. This indicates limited capability in extracting features from dense small targets, falling short of full recognition and information capture. Addressing this limitation should be a key focus for future research. Thanks for your constructive comments.\n\n**Your feedback is highly constructive and deeply encouraging to us. We sincerely thank you once again. If you have any further questions or concerns, we would be truly grateful for your guidance.**"}]

Author rebuttal:

Meta-review: This paper proposes a new high resolution remote-sensing dataset, a new method for training by suppressing irrelevant background tokens, and a new multimodal LLM, GeoLlava. All reviewers find the contributions significant, and any concerns have been addressed by the authors in the rebuttal.
Decision: Accept (spotlight)