UrbanCross: Enhancing Satellite Image-Text Retrieval with Cross-Domain Adaptation

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-03-28 17:53:17

Abstract: Urbanization challenges underscore the necessity for effective satellite image-text retrieval methods to swiftly access specific information enriched with geographic semantics for urban applications. However, existing methods often overlook significant domain gaps across diverse urban landscapes, primarily focusing on enhancing retrieval performance within single domains. To tackle this issue, we present UrbanCross, a new framework for cross-domain satellite image-text retrieval. UrbanCross leverages a high-quality, cross-domain dataset enriched with extensive geo-tags from three countries to highlight domain diversity. It employs the Large Multimodal Model (LMM) for textual refinement and the Segment Anything Model (SAM) for visual augmentation, achieving a fine-grained alignment of images, segments and texts, yielding a 10\% improvement in retrieval performance. Additionally, UrbanCross incorporates an adaptive curriculum-based source sampler and a weighted adversarial cross-domain fine-tuning module, progressively enhancing adaptability across various domains. Extensive experiments confirm UrbanCross's superior efficiency in retrieval and adaptation to new urban environments, demonstrating an average performance increase of 15\% over its version without domain adaptation mechanisms, effectively bridging the domain gap. Our code is publicly accessible, and the dataset will be made available at https://anonymous.4open.science/r/UrbanCross/.

Main paper: 
 introduction: 
 Enriched with geographic details, satellite imagery serves as a vital resource for comprehending the functionality of a region, with a variety of applications ranging from poverty assessment [13, 14] , crop yield prediction [28, 29] , to urban region profiling [10, 38] . Satellite Image-Text Retrieval aims to retrieve specific satellite images from an image pool based on text descriptions, and vice versa [3] , which has gathered increased attention with global urbanization. The key to the success of such retrieval lies in effectively harmonizing satellite image and textual data within urban complexities [47] . A row of houses with identical roofsGenerator source of solar, surrounded by road of service, buildingA row of barns used to store grainMan made storage tank, land use of industrial with industrial oilFigure1: Introduction of Domain Adaptation and Geo-Tags Related literature can be categorized into two main paradigms [15, 17, 46] . The first stream, known as Content-based methods, is centered on generating precise captions for satellite images via generative models, converting image-text retrieval to a text-to-text task [18, 36] . However, these methods usually encounter detail loss in complex scenes and heavily rely on extensive annotated datasets. In contrast, Embedding-based methods aim to leverage pre-trained encoders to align images and texts within a unified semantic space, ensuring better modal interaction for the retrieval task. These approaches explore novel mechanisms, such as self-attention and Contrastive Language-Image Pre-training (CLIP), to enhance representation learning [19, 27, 33] and modality interaction [8, 40] .Common Semantic SpaceThough promising, previous approaches mostly assume uniformity in satellite image-text pairs across varied landscapes. This assumption, however, may lead to inferior model performance when dealing with data distribution shifts. Figure 1 (a) depicts an example, where geographical differences, such as the varying prevalence of solar power plants between Finland and Spain due to distinct solar exposure levels, may result in the model trained on Finnish data incorrectly classifying Spanish solar plants as residential areas. This underscores the critical need for cross-domain adaptation to ensure semantically equivalent feature alignment across geographies.In this paper, we identify two key perspectives to enhance satellite image-text retrieval with cross-domain adaptation • Data perspective: Learning domain-invariant features is the prerequisite for cross-domain adaptation. When understanding urban regions, relying solely on visual features may lead to misinterpretation, as illustrated in Figure 1 (b). Fortunately, realworld satellite images are often accompanied by other descriptions or metadata, such as geo-tags, which are texts that can generalize well in all countries. By incorporating such auxiliary information, we can effectively identify and complement original vision representations, thereby enhancing the generalization ability of retrieval models. • Model perspective: Enhancing the model's adaptability to domain shifts requires improving the identification and adjustment capabilities to data variances across domains, through a robust and domain-aware framework. Our approach dynamically adjusts model parameters in response to identified domain-specific features, thereby significantly improving the adaptability and accuracy of the retrieval system. In response to these challenges, we present a novel framework termed UrbanCross, which enhances the embedding-based retrieval paradigm with cross-domain adaptability. From the data perspective, we augment data representations with two endeavors. Externally, our model integrates geo-tags with Large Multimodal Model (LMM) to generate visually rich and semantically accurate image captions. Internally, we employ Segment Anything (SAM) to extract fine-grained visual features from the input image itself, eliminating irrelevant elements and aligning these with corresponding text to enhance data quality across visual and language domains.From the model perspective, we devise two innovative modules: Adaptive Curriculum-based Source Sampler, which initially samples source data based on similarity to the target domain, followed by the Adversarial Cross-Domain Image-Text Fine-tuning Module for subsequent fine-tuning. This integrated strategy ensures a seamless transition from simpler to complex samples, applying weighting to align with domain-specific traits, thus effectively addressing the challenges posed by diverse data distributions across domains.Our contributions are summarized as follows: • Data Augmentation: By integrating LMM with geo-tags to enrich textual descriptions and employing SAM for precise visual segmentation, UrbanCross significantly enhances both visual and textual accuracy, ensuring contextual and semantic understanding, resulting in higher-quality data representations. [7, 11, 22, 30, 40, 42, 43] . Techniques such as GaLR [43] and KCR [22] have advanced text comprehension by integrating global-local image features and domain-specific knowledge, respectively. Meanwhile, Vision-Language Pre-training (VLP) models like CLIP [27] face challenges in processing diverse urban satellite imagery features. Innovations in high-resolution imaging [33] and multilingual text processing [2] highlight VLP's adaptability, supported by foundational models such as RSGPT [12] and SkyEyeGPT [44] . However, these methods often neglect the data distribution variability across different domains, an issue UrbanCross aims to address.Geo-Tags: amenity of school, road of residential; surrounded by road of service;  
large multimodal model and segment anything model.: 
 With the rapid advancements in Large Language Model (LLM), the incorporation of visual information into these models is increasingly prominent. Recent developments, including MiniGPT-4 [4] , LLaVA [20] , and InstructBLIP [9] , have expanded parameters and training data, contributing to the evolution of LMM. Models such as mPLUG-Owl [39] , Shikra [6] , and KOSMOS-2 [26] have introduced techniques aimed at mitigating hallucinations in LMM. Concurrently, SAM [16] , renowned for its robust image segmentation capabilities, finds widespread application across various domains. This includes urban infrastructure analysis [1] , UV-SAM for urban segmentation [45] , and RSPrompter for satellite image-based instance segmentation [5] . Yet, the potential of these models in satellite image-text retrieval remains largely untapped. Hence, UrbanCross leverages the strengths of LMM and SAM to enrich the image-text dataset, facilitating multimodal alignment and cross-domain adaptation.Research. Domain adaptation, a critical subfield of transfer learning, is pivotal in overcoming labeled data scarcity and ensuring model generalization across varied scenarios [25] . In urban areas, the application of domain adaptation is invaluable, leveraging heterogeneous data to markedly enhance model performance in areas such as traffic forecasting [31] , environmental monitoring [35] , and air quality prediction [34] . Despite its widespread application in urban contexts, domain adaptation strategies remain underexplored in satellite image-text retrieval. UrbanCross addresses this gap by incorporating domain adaptation strategies for satellite imagery and its associated texts, thus enhancing adaptability across global urban landscapes. The satellite image shows a school facility characterized by a cluster of buildings with varying roof designs, encircled by service and residential roads, complemented by green spaces and a designated parking area. 
methodology: 
 The UrbanCross framework (Figure 2 ) has three main stages.• Image Captioning and Segmentation: At stage one, LMM, augmented with geo-tags, produces detailed descriptions for satellite images, yielding semantically enriched image-text pairs. Simultaneously, SAM [16] 3.1.1 Text Augmentation with LMM and Geo-Tags. To ensure highquality textual descriptions for experiments, we employ the In-structBLIP [9] LLM for text generation. However, directly inputting satellite images into the LLM can result in insufficient semantic details. As illustrated in phase 1 of Figure 2 , although the model captures general features like "cluster of buildings", "parking areas", and "green spaces", it often omits specific details such as "residential roads" or "school facilities". To address this, geo-tags are integrated to enhance visual accuracy and provide additional contextual information, resulting in n more detailed and precise image captions.In satellite image-text retrieval tasks, precisely matching text details with image features is crucial. However, extraneous elements in satellite images often reduce accuracy. We address this using SAM [16] , to isolate key features from images. We initially set an area threshold in SAM's "everything" mode to exclude overly small and non-essential segments, for further improving visual-language alignment.EQUATIONHere, S 𝑓 𝑖𝑙𝑡𝑒𝑟𝑒𝑑 , derived by applying an area threshold T 𝑎 to the original segment collection S, excludes smaller segments. CLIP [27] is then used to calculate the similarity between text and segments.EQUATIONEQUATIONwhere 𝑠𝑐𝑜𝑟𝑒 (s 𝑖 , t) measures segment-text similarity, and 𝝓𝑠𝑒𝑔 and 𝝍𝑡𝑒𝑥𝑡 are the respective CLIP encoders, segments meeting this criterion are weighted in the final embedding.EQUATIONwhere w 𝑖 = 𝑠𝑐𝑜𝑟𝑒 (s 𝑖 , t) serves as the weight for each segment, prioritizing the most relevant ones, ensuring a focused and semantically coherent alignment with textual descriptions. EQUATION𝑬 𝐼 (𝑖 ) = Softmax(EQUATIONEQUATIONwhere W 𝑂 is the learnable output projection matrix, 𝑁 ℎ is the number of heads, and Concat indicates concatenation operation. Self-attention dynamically weights the interactions between patches, enhanced by residual connections and layer normalization, producing the latent visual representation:EQUATION)Building on the dynamic patch representations achieved through self-attention and layer normalization, this approach also incorporates a learnable image [CLS] token to further enhance visual encodings for complex cross-modality tasks. To achieve finer feature alignment between visual and language modalities, we enhance retrieval accuracy by creating a shared embedding space that bridges the semantic gap between satellite images (E𝐼 ), image segments (E𝑆), and text (E 𝑇 ). This uses pairwise contrastive loss to align images and segments with text, assessing their similarities, respectively:• Image-to-Text Contrastive Loss (L 𝑖𝑚𝑔2𝑡𝑒𝑥𝑡 ): Reduces disparities between matched image-text pairs while enhancing the distinction for mismatches. • Segment-to-Text Contrastive Loss (L 𝑠𝑒𝑔2𝑡𝑒𝑥𝑡 ): Improves the alignment between image segments and corresponding text, promoting proximity for related pairs while increasing separation for unrelated ones. The total objective function, L = L𝑖𝑚𝑔2𝑡𝑒𝑥𝑡 +L𝑠𝑒𝑔2𝑡𝑒𝑥𝑡, guides the model in tightly aligning images and segments with textual descriptions. By employing margin-driven separation in the embedding space, this contrastive approach enhances the model's ability to differentiate between related and unrelated pairs. ity matrix W 1 ∈ R 𝑛 𝑡 ×𝑛 𝑠is then computed to assess the pairwise similarities between the target and source batches, quantifying the degree of alignment between features.EQUATION)Based on W 1 , we extract a new source subset by selecting the top 𝐾% most similar pairs for each target. The curriculum learning strategy incrementally increases 𝐾 from 0 by 20% across five epochs, progressing from simpler to more complex samples. Specifically, the range initially covers the first 20%, and then expanding in subsequent epochs to include the next 20% increments, i.e., from 20% to 40%, then from 40% to 60%, and so on. This method ensures progressive exposure from simpler to more complex samples, enhancing adaptation without introducing additional overhead. Additionally, W 2 ∈ R 𝑛×𝑛 , derived from W 1 , reflects evolving similarity between selected source and target batches. Continuously updated, these matrices guide the choice of increasingly challenging source batches. Consequently, the ACSS-selected matrix supports weighted adversarial learning, enhancing domain adaptation. EQUATIONHere, 𝑑 (•, •) denotes the cosine similarity distance, with 𝑎, 𝑝, and 𝑛 representing anchor, positive, and negative samples, respectively. The term 𝛼 refers to the margin. A domain discriminator 𝐷, implemented by a three-layer MLP, is utilized to align source and target domain distributions. During training, 𝐷 minimizes binary cross-entropy loss between its predictions and ground truth labels, indicating real or generated data. With source ( Ẽ𝑠 𝑇 , Ẽ𝑠 𝐼 ) and target ( Ẽ𝑡 𝑇 , Ẽ𝑡 𝐼 ) features as input, 𝐷 is adversarially trained with the encoder to predict domain labels. The encoders for text and images are fine-tuned to produce image-text features indistinguishable by 𝐷, which seeks to maximize the probability of correct predictions.EQUATION) 
experiments: 
 In [21] and RSITMD [41] datasets, which comprise 10,921 and 4,743 images, respectively. Our domain adaptation studies utilized the Skyscript benchmark dataset [37] , which features 5.2 million image-text pairs globally. To improve UrbanCross's adaptability across diverse urban environments, we selected high-resolution images from Spain, Germany, and Finland (GSD ≤ 0.5 m/pixel), resulting in the creation of UC-Spain, UC-Finland, and UC-Germany datasets. These datasets contain 46,041 pairs of 1,621 types of geotags from various Spanish regions (ranging from the capital to smaller towns), 165,128 pairs of 3,033 types of geo-tags from Berlin, Germany, and 58,783 pairs of 5,826 types of geo-tags covering Finland, demonstrating a commitment to geographic diversity and data quality. (MeanR), providing a comprehensive overview of performance, computes the average of R@1, R@5, and R@10 for both image-totext and text-to-image satellite retrieval tasks. Furthermore, to evaluate domain adaptation, UrbanCross is pretrained on data from one country and tested in another, assessing the model's capacity to generalize to different urban environments. Comparing MeanR performance-direct model transfer versus finetuning with a subset of target domain data-highlights significant enhancements in global urban analysis effectiveness.Our framework is trained on NVIDIA A800 GPUs utilizing the Adam Optimizer, with hyperparameter adjustments based on each epoch's performance on the validation set. During the multimodal pretraining stage, the initial learning rate is established at 1e-6, implementing a dynamic reduction strategy with a weight decay of 0.3 every 10 epochs. Batch sizes are set at 40, spanning 15 epochs, with each image keeping segments num as 6. Threshold 𝑇 𝑎 and 𝑇 𝑠 are set to 0.2. In the domain adaptation fine-tuning phase, the learning rate is set at 1e-7, spanning 5 epochs. The ratio, defined by the target batch size of 16 to the source batch size of 80, is 0.2. 𝛽 of the loss function is set to 1. For the pretraining dataset, we split train:val:test=7:1:2, while for the fine-tuning dataset, we split train:val:test=2:1:7, to facilitate domain-adaptation fine-tuning under limited data scenarios.An empirical evaluation was performed to assess the performance of various models on satellite image-text retrieval tasks, utilizing the RSICD and RSITMD datasets. This evaluation aimed to benchmark the effectiveness of the UrbanCross model against conventional methodologies. Additional experiments were conducted on the UC-Spain, UC-Finland, and UC-Germany datasets to establish benchmarks for future research. Importantly, this comparison excluded considerations of domain adaptation. The UrbanCross variant deployed in these assessments was UrbanCross-MMA (Multi-Modal Alignment), which excluded the domain adaptation stage. Furthermore, an ablation study was conducted to explore the impact of image segmentation on enhancing retrieval accuracy. The detailed results of these experiments are presented in Table 1 .• UrbanCross exhibits improved retrieval accuracy, surpassing the recent baseline, PIR, with mean recall improvements of 50.3% on RSICD dataset and 29.8% on RSITMD dataset, under equivalent training and testing conditions. Besides, it also demonstrates a modest enhancement over RemoteCLIP, which is trained on a larger dataset, highlighting its effective learning capabilities. • The impact of SEG varies across datasets, depending on the quality of the data. For the RSICD and RSITMD datasets, adding segments for alignment decreases mean recall by 1.7% and 1.2%, respectively. This reduction is primarily attributed to the insufficient semantic information in the text, which hinders alignment with the fine-grained segment features, leading to broader contextual matching and, consequently, diminished returns. • Conversely, the SEG-inclusive model excels in UC datasets with rich annotations, showing mean recall improvements of 10.9% in UC-Spain, 9.3% in UC-Finland, and 9.8% in UC-Germany. These gains highlight SEG's importance in datasets requiring finegrained visual-textual matching. Our method, characterized by The optimization process of UrbanCross's hyperparameters is meticulously designed to enhance the mean recall, a critical performance metric. This process is comprehensively visualized in Figure 5 , which delineates the adjustments and their impacts during both the pre-training and fine-tuning stages. Through systematic experimentation during both the pre-training and fine-tuning stages, we identified optimal settings that maximize mean recall while ensuring model stability.To demonstrate our method's cross-domain efficacy, we provide two illustrative examples representing distinct settings: Finland to Spain and Germany to Finland, as depicted in Figure 6 .For the image query of an industrial roof with solar panels, using the pre-trained model from Finland directly in Spain, without domain adaptation, resulted in inferior outcomes and failed to accurately capture the solar panel concept. This discrepancy can be attributed to the climatic differences between Finland and Spain. Finland experiences limited annual sunlight and extended periods of darkness, particularly in winter, while Spain enjoys abundant sunshine and widespread solar energy adoption, resulting in frequent solar panel depictions in image-text pairs. By fine-tuning the model through domain adaptation with a small dataset from Spain, we achieved accurate textual descriptions. An analysis of the top four results reveals a consensus regarding the solar panel concept.In the second example, assessing the model's ability to interpret test query images underscores features characteristic of Finland, such as extensive snow cover and a vibrant winter sports scene featuring skiing, ice hockey, and ice skating. Conversely, the absence of comparable samples in Germany results in pre-fine-tuning images that emphasize superficial attributes like snow and whiteness, potentially yielding irrelevant results unrelated to ice sports. These images might portray snow-covered locations with no specific association with ice sports activities. Through fine-tuning, the model significantly enhances its precision in associating search text with locations related to ice sports. 
conclusion and future work: 
 This paper introduced UrbanCross, a novel framework designed for cross-domain satellite image-text retrieval, achieving enhancements at both data and model levels. At the data level, it integrates geo-tags and an LMM to enrich textual semantics and diversity, and employs SAM to maintain highly relevant visual features, thus ensuring improved alignment with text. At the model level, it incorporates fine-grained feature fusion for enhanced modal alignment and introduces a novel domain adaptation module that combines a Curriculum-based Source Sampler with Weighted Adversarial Cross-Domain Fine-tuning to effectively address the domain gap across various countries. Experimental results demonstrate that UrbanCross surpasses baseline models in retrieval performance and exhibits remarkable adaptability to diverse urban landscapes.We aspire that this work will inspire future research in crossdomain satellite image-text retrieval frameworks on the following aspects: 1) Designing more precise curriculum learning strategy; 2) Exploring zero-shot cross-domain satellite image-text retrieval; 3) Empowering knowledge update via external knowledge base.  
 
 Appendix:  


Decision: This paper introduces a novel framework called UrbanCross, designed for satellite image-text retrieval tasks. It addresses domain gaps across diverse urban landscapes through cross-domain adaptation. All reviewers have acknowledged the effectiveness of the proposed framework and its innovative integration of LLM and SAM  to enhance the image-text alignment in the training data. Common concerns were initially raised regarding the model’s complexity and the evaluation of hyperparameters. However, most of these issues have been satisfactorily addressed after the rebuttal. Given its novelty and impressive performance, I recommend accepting this paper.

Reviews: review: paper_summary: paper_summary: The paper addresses the need for effective satellite image-text retrieval methods for urban applications, highlighting the often overlooked domain gaps across diverse urban landscapes. To address this, the authors present UrbanCross, a new framework for cross-domain satellite image-text retrieval. UrbanCross utilizes a high-quality, cross-domain dataset with extensive geo-tags from three countries to emphasize domain diversity. It employs the Large Multimodal Model (LMM) for textual refinement and the Segment Anything Model (SAM) for visual augmentation, achieving a 10% improvement in retrieval performance. Additionally, it incorporates an adaptive curriculum-based source sampler and a weighted adversarial cross-domain fine-tuning module to enhance adaptability across various domains. Experiments show a 15% performance increase over its non-adaptive version, effectively bridging the domain gap.
strength_weakness: strength_weakness: The paper addresses the need for effective satellite image-text retrieval methods for urban applications, highlighting the often overlooked domain gaps across diverse urban landscapes. To address this, the authors present UrbanCross, a new framework for cross-domain satellite image-text retrieval. UrbanCross utilizes a high-quality, cross-domain dataset with extensive geo-tags from three countries to emphasize domain diversity. It employs the Large Multimodal Model (LMM) for textual refinement and the Segment Anything Model (SAM) for visual augmentation, achieving a 10% improvement in retrieval performance. Additionally, it incorporates an adaptive curriculum-based source sampler and a weighted adversarial cross-domain fine-tuning module to enhance adaptability across various domains. Experiments show a 15% performance increase over its non-adaptive version, effectively bridging the domain gap.
limitations: limitations: 1.	In Section 1, it is recommended to add “:”at the end of this sentence “In this paper, we identify two key perspectives to enhance satellite image-text retrieval with cross-domain adaptation”.
2.	Please check the following part. In Section 3.11, it is suggested to remove the n in the last sentence.
3.	It is recommended to write “where” at the beginning of a new line when describing the meaning of symbols in formula (2)(3)(4)(7).
4.	Each symbol in the paper should be explained as much as possible, such as what n represents in Equation (4).
5.	In Section 3.2.3, the author used a subscript for ET. Should EI and ES also use subscripts? Please check the related part
6.	If there is an overall loss function, it would be better to include it.
7.	Mean Recall has been defined as MeanR in experimental section, it is better to denote 	Mean Recall with MeanR in performance tables.
paper_summary: paper_summary: This paper introduces a novel UrbanCross framework which enhances satellite image-text retrieval by addressing domain gaps across diverse urban landscapes through cross-domain adaptation. It leverages high-quality texts, geo-tags, and LMM integration to improve image-text alignment, especially in datasets with thorough semantics and contextual information. The framework demonstrates significant adaptability improvements in domain shifts across various countries, with mean recall increases of 15.0% for Finland to Spain, 15.2% for Germany to Spain, and 15.5% for Germany to Finland when fully configured with SS, CL, and AT components. The fine-tuning stage optimizes model performance during domain adaptation by adjusting learning rates and batch sizes to maximize mean recall while ensuring model stability. Additionally, qualitative analysis showcases the efficacy of domain adaptation in improving image-text alignment, as demonstrated in examples of Finland to Spain and Germany to Finland transfers.
strength_weakness: strength_weakness: a.	This paper significantly enhances both visual and textual accuracy by integrating LMM with geo-tags to enrich textual descriptions and employing SAM for precise visual segmentation.
b.	This paper introduces a curriculum-based source sampler and a weighted adversarial fine-tuning module which improves domain adaptation.
c.	Extensive experimental results demonstrate the compelling performance of the method compared to the SOTA baselines.
limitations: limitations: a.	The method utilizes Vit-L-14 as the backbone, what if replace it with Vit-B-16? Since it is noted that the RemoteCLIP which utilizes Vit-B-16 also achieves good results, this paper should report the backbone results for fair comparison.
b.	This paper should report the results of other methods on the UC dataset, for a more solid comparison. And the UC dataset was also used in the ablation experiment, are the results of the ablation experiment representative of the validity on the other two datasets (i.e. RSICD and RSITMD)?
c.	In the hyperparametric experiments, it is noted that optimal performance is reached with epoch = 15 during Pre-training, as shown in Figure 5 (b), but this paper does not report performance with epoch less than 15, which may not be a good proof that 15 is optimal.

Typos and minors
It would be advisable to check the entire manuscript with a view to the typos, grammar and syntax. For example, singular and plural expressions should be checked.
paper_summary: paper_summary: This paper introduces UrbanCross, an innovative framework for cross-domain satellite image-text retrieval that addresses the diversity challenge in urban landscapes. By leveraging a multi-country dataset and advanced models such as LMM and SAM, it achieves a 10% improvement in retrieval performance. Additionally, it includes adaptive and adversarial fine-tuning mechanisms that enhance adaptability across domains, resulting in an average 15% performance increase over non-adapted versions.
strength_weakness: strength_weakness: 1. This work introduces a novel approach to cross-domain satellite image-text retrieval. It leverages high-quality cross-domain datasets enriched with geo-tags and employs advanced models like the Large Multimodal Model (LMM) and the Segment Anything Model (SAM) for enhanced visual and textual alignment.
2. Extensive experiments demonstrate that UrbanCross achieves significant improvements in retrieval performance. It enhances retrieval efficiency by 10% and outperforms methods lacking domain adaptation mechanisms by 15% on average.
3. The paper clearly outlines its contributions, including data augmentation, cross-domain adaptation, and extensive experimentation. This makes it easy for readers to understand the key innovations and improvements introduced by UrbanCross.
limitations: limitations: 1. The proposed framework is comprehensive and involves multiple sophisticated components, such as LMM, SAM, adaptive curriculum-based source sampling, and weighted adversarial fine-tuning. However, it is challenging to implement in realistic scenes.

2.The hyperparameter analysis is inadequate. The authors conducted experiments on several hyperparameters, selecting only three values for comparison. It is recommended that the analysis be expanded with additional explanations.
paper_summary: paper_summary: This manuscript introduces UrbanCross, a novel framework addressing the need for efficient satellite image-text retrieval methods in urban applications. Unlike existing methods focusing on single domains, UrbanCross emphasizes cross-domain retrieval, leveraging a diverse dataset from three countries with geo-tags. It employs the Large Multimodal Model (LMM) for textual refinement and the Segment Anything Model (SAM) for visual augmentation, resulting in fine-grained alignment of images, segments, and texts. UrbanCross also incorporates an adaptive curriculum-based source sampler and a weighted adversarial cross-domain fine-tuning module, enhancing adaptability across domains.
strength_weakness: strength_weakness: 1.UrbanCross proposes a novel cross-domain adaptation mechanism for handling data distribution disparities across different geographical locations and urban environments. By employing an adaptive curriculum-based source sampler and a weighted adversarial cross-domain fine-tuning module, UrbanCross effectively bridges the gaps between different domains.

2.UrbanCross enriches text descriptions by integrating geographical labels and leveraging a large-scale multi-modal model (LMM). Additionally, it employs Segment Anything Model (SAM) for precise visual segmentation, enhancing the model's understanding and recognition of urban features.
limitations: limitations: 1. While UrbanCross demonstrates excellent cross-domain adaptability, the paper may not have fully explored the model's generalization capabilities across a broader range of geographical and cultural contexts. If testing is limited to specific countries or cities, the model's effectiveness in other regions remains an open question.

2. The paper may lack detailed discussion on the computational complexity of the UrbanCross framework, including resource consumption during training and inference.  Given the utilization of Large Multimodal Models (LMM) and Segment Anything Model (SAM), there might be high computational resource requirements, potentially limiting the model's application in resource-constrained environments. Efficiency and scalability of the model are equally crucial for practical applications.

3. The dataset used in the paper may have limitations in certain aspects, such as potentially lacking sufficient diversity or containing biases. The authors could discuss the potential biases in the dataset and the impact they may have on the model's performance.

4. For certain applications like emergency response or real-time monitoring, the model's real-time performance is crucial. The paper may not have assessed UrbanCross's performance in real-time environments.

5. While the paper thoroughly investigates hyperparameters, it may not explore automated hyperparameter automated hyperparameter optimization methods like Bayesian optimization, which could save significant time and resources in practical applications.

6. The paper may not have conducted sufficient comparisons with current state-of-the-art techniques, such as the method proposed in "Knowledge-Aided Momentum Contrastive Learning for Remote-Sensing Image Text," and also may not have thoroughly explained the rationale behind selecting specific baseline models for comparison.

7. It seems like there might be some misstatements in the paper, such as the confusion between "i" and "j" on line 417, the correction of "cross-domain" instead of "cross-modal" on line 458, inconsistency in the usage of "target batch" and "source batch" with their corresponding "nt" and "ns" in Figure 3, and discrepancies between the statement on lines 786-788 and the results in Table 2. These errors should be addressed for clarity and accuracy in the paper.