Semantic segmentation of remote sensing imagery (RSI) is a fundamental task that aims at assigning a category label to each pixel. To pursue precise segmentation with one or more fine-grained categories, semantic segmentation often requires holistic segmentation of whole-scene RSI (WRI), which is normally characterized by a large size. However, conventional deep learning methods struggle to handle holistic segmentation of WRI due to the memory limitations of the graphics processing unit (GPU), thus requiring to adopt suboptimal strategies such as cropping or fusion, which result in performance degradation. Here, we introduce the Robust End-to-end semantic Segmentation architecture for whole-scene remoTe sensing imagery (REST). REST is the first intrinsically endtoend framework for truly holistic segmentation of WRI, supporting a wide range of encoders and decoders in a plugandplay fashion. It enables seamless integration with mainstream semantic segmentation methods, and even more advanced foundation models. Specifically, we propose a novel spatial parallel interaction mechanism (SPIM) within REST to overcome GPU memory constraints and achieve global context awareness. Unlike traditional parallel methods, SPIM enables REST to process a WRI effectively and efficiently by combining parallel computation with a divideandconquer strategy. Both theoretical analysis and experiments demonstrate that REST attains nearlinear throughput scalability as additional GPUs are employed. Extensive experiments demonstrate that REST consistently outperforms existing cropping-based and fusion-based methods across a variety of scenarios, ranging from single-class to multi-class segmentation, from multispectral to hyperspectral imagery, and from satellite to drone platforms. The robustness and versatility of REST are expected to offer a promising solution for the holistic segmentation of WRI, with the potential for further extension to large-size medical imagery segmentation.

Large-size very-high-resolution (VHR) remote sensing imagery has emerged as a critical data source for high-precision vector mapping of multi-scale geographical elements such as building, water, road and etc. When dealing with the large-size image, due to the limited memory of GPU, the deep learning-based vector mapping methods often employ the sliding block strategy. This inevitably leads to the degenerated performance because of the stitching difficulty of the sliding blocks' vector mapping results. Therefore, it is necessary to conduct full-scope vector mapping via mining the consistent cue in large-size remote sensing imagery. To this end, this paper presents a novel global context-aware local point optimization method. To leverage the global context, this paper proposes a novel pyramid fusion network (PFNet) to conduct semantic segmentation of the large-size image in an end-to-end manner. Under the constraint of the global semantic segmentation result, a new inflection-point perception network (IPNet) is proposed to generate a set of stable points to depict the boundary of each element. Extensive experiments on building, water and road datasets, where each image has over 100 million pixels, show that our method obviously outperforms the existing methods.

Traditional Remote Sensing Foundation models (RSFMs) are pre-trained with a data-centralized paradigm, through self-supervision on large-scale curated remote sensing data. For each institution, however, pre-training RSFMs with limited data in a standalone manner may lead to suboptimal performance, while aggregating remote sensing data from multiple institutions for centralized pre-training raises privacy concerns. Seeking for collaboration is a promising solution to resolve this dilemma, where multiple institutions can collaboratively train RSFMs without sharing private data. In this paper, we propose a novel privacy-preserved pre-training framework (FedSense), which enables multiple institutions to collaboratively train RSFMs without sharing private data. However, it is a non-trivial task hindered by a vicious cycle, which results from model drift by remote sensing data heterogeneity and high communication overhead. To break this vicious cycle, we introduce Federated Mutual-guidance Learning. Specifically, we propose a Server-to-Clients Guidance (SCG) mechanism to guide clients updates towards global-flatness optimal solutions. Additionally, we propose a Clients-to-Server Guidance (CSG) mechanism to inject local knowledge into the server by low-bit communication. Extensive experiments on four downstream tasks demonstrate the effectiveness of our FedSense in both full-precision and communication-reduced scenarios, showcasing remarkable communication efficiency and performance gains.

With the increasing resolution of remote sensing imagery (RSI), large-size RSI has emerged as a vital data source for high-precision vector mapping of geographic objects. Existing methods are typically constrained to processing small image patches, which often leads to the loss of contextual information and produces fragmented vector outputs. To address these, this paper introduces HoliTracer, the first framework designed to holistically extract vectorized geographic objects from large-size RSI. In HoliTracer, we enhance segmentation of large-size RSI using the Context Attention Net (CAN), which employs a local-to-global attention mechanism to capture contextual dependencies. Furthermore, we achieve holistic vectorization through a robust pipeline that leverages the Mask Contour Reformer (MCR) to reconstruct polygons and the Polygon Sequence Tracer (PST) to trace vertices. Extensive experiments on large-size RSI datasets, including buildings, water bodies, and roads, demonstrate that HoliTracer outperforms state-of-the-art methods. Our code and data are available in https://github.com/vvangfaye/HoliTracer.

Scene graph generation (SGG) in satellite imagery (SAI) benefits promoting understanding of geospatial scenarios from perception to cognition. In SAI, objects exhibit great variations in scales and aspect ratios, and there exist rich relationships between objects (even between spatially disjoint objects), which makes it attractive to holistically conduct SGG in large-size very-high-resolution (VHR) SAI. However, there lack such SGG datasets. Due to the complexity of large-size SAI, mining triplets subject, relationship, object heavily relies on long-range contextual reasoning. Consequently, SGG models designed for small-size natural imagery are not directly applicable to large-size SAI. This paper constructs a large-scale dataset for SGG in large-size VHR SAI with image sizes ranging from 512 × 768 to 27,860 × 31,096 pixels, named STAR (Scene graph generaTion in lArge-size satellite imageRy), encompassing over 210K objects and over 400K triplets. To realize SGG in large-size SAI, we propose a context-aware cascade cognition (CAC) framework to understand SAI regarding object detection (OBD), pair pruning and relationship prediction for SGG. We also release a SAI-oriented SGG toolkit with about 30 OBD and 10 SGG methods which need further adaptation by our devised modules on our challenging STAR dataset. The dataset and toolkit are available at: https://linlin-dev.github.io/project/STAR.

Deep networks have been widely used in remote sensing building change detection (BCD) and achieved good performances. However, the BCD networks are vulnerable to adversarial attacks. Previous physical adversarial attacks in remote sensing focused on image-level tasks and instance-level tasks, we propose a generalizable physical attacks (GPA) framework for the pixel-level BCD task. The GPA framework is in the joint task-instance space to generate the universal physical adversarial patch which can cheat unseen models. It involves white-box attacks and black-box attacks. To solve the problem of the singular adversarial patch position and the singular target task in existing works, we propose an adaptive adversarial patch position module and present a feature loss for bi-temporal remote sensing images in white-box attacks. To enhance the generalization of the patch, we propose a method in the joint task-instance space to learn the black-box adversarial patch. Extensive experiments on the widely used remote sensing image BCD dataset shows the proposed adversarial attacks against BCD achieve better performance compared with state-of-the-art methods. In particular, our GPA elevatesASRpandCD_Ratefor all buildings by an average of 29.31%, 13.92% in the white-box scenario, and 3.92%, 3.59% in the strict black-box scenario. Real-world experiments with printed patches on physical building models further demonstrate that our GPA can attack the BCD networks. The source code and associated datasets are available at https://github.com/hhhh420/adversarial patch BCD.

Global surface water detection in very-high-resolution (VHR) satellite imagery can directly serve major applications such as refined flood mapping and water resource assessment. Although achievements have been made in detecting surface water in small-size satellite images corresponding to local geographic scales, datasets and methods suitable for mapping and analyzing global surface water have yet to be explored. To encourage the development of this task and facilitate the implementation of relevant applications, we propose the GLH-water dataset that consists of 250 satellite images and 40.96 billion pixels labeled surface water annotations that are distributed globally and contain water bodies exhibiting a wide variety of types (e.g. , rivers, lakes, and ponds in forests, irrigated fields, bare areas, and urban areas). Each image is of the size 12,800 × 12,800 pixels at 0.3 meter spatial resolution. To build a benchmark for GLH-water, we perform extensive experiments employing representative surface water detection models, popular semantic segmentation models, and ultra-high resolution segmentation models. Furthermore, we also design a strong baseline with the novel pyramid consistency loss (PCL) to initially explore this challenge, increasing IoU by 2.4% over the next best baseline. Finally, we implement the cross-dataset generalization and pilot area application experiments, and the superior performance illustrates the strong generalization and practical application value of GLH-water dataset. Project page: https://jack-bo1220.github.io/project/GLH-water.html

Prior studies on Remote Sensing Foundation Model (RSFM) reveal immense potential towards a generic model for Earth Observation. Nevertheless, these works primar-ily focus on a single modality without temporal and geo-context modeling, hampering their capabilities for diverse tasks. In this study, we present SkySense, a generic billion-scale model, pretrained on a curated multimodal Remote Sensing Imagery (RSI) dataset with 21.5 million temporal sequences. SkySense incorporates a factorized multimodal spatiotemporal encoder taking temporal sequences of opti-cal and Synthetic Aperture Radar (SAR) data as input. This encoder is pretrained by our proposed Multi-Granularity Contrastive Learning to learn representations across different modal and spatial granularities. To further enhance the RSI representations by the geo-context clue, we introduce Geo-Context Prototype Learning to learn region-aware prototypes upon RSI's multimodal spatiotemporal features. To our best knowledge, SkySense is the largest Multi-Modal RSFM to date, whose modules can be flexibly combined or used individually to accommodate various tasks. It demonstrates remarkable generalization capabilities on a thor-ough evaluation encompassing 16 datasets over 7 tasks, from single- to multimodal, static to temporal, and classification to localization. SkySense surpasses 18 recent RSFMs in all test scenarios. Specifically, it outperforms the latest models such as GFM, SatLas and Scale-MAE by a large margin, i.e., 2.76%, 3.67% and 3.61% on average respectively. We will release the pretrained weights to facilitate future research and Earth Observation applications.

Obtaining accurate and timely flood information is crucial for effective disaster management and response. To address the limitations of existing methods in terms of accuracy and model robustness, this research significantly improves the accuracy and stability of flood detection by integrating domain knowledge into the Remote Sensing Foundation Model (RSFM). Specifically, we employ advanced RSFM to focus on extracting spatial texture features from images after super-resolution. The Automatic Water Extraction Index (AWEI) is introduced to leverage spectral information from multi-spectral imagery, while model fusion techniques further enhance the accuracy of segmentation results. Moreover, we incorporate prior knowledge such as land use products and Digital Elevation Models (DEM) for knowledge rules-driven post-processing, refining the final flood detection results. Experimental results demonstrate that our approach achieve the second-place ranking in the 2024 IEEE GRSS Data Fusion Contest (DFC) Track 2 test phase (F1: 88.25%), highlighting the effectiveness and competitiveness of our method.

Semi-supervised semantic segmentation focuses on the exploration of a small amount of labeled data and a large amount of unlabeled data, which is more in line with the demands of real-world image understanding applications. However, it is still hindered by the inability to fully and effectively leverage unlabeled images. In this paper, we reveal that cross-window consistency (CWC) is helpful in comprehensively extracting auxiliary supervision from unlabeled data. Additionally, we propose a novel CWC-driven progressive learning framework to optimize the deep network by mining weak-to-strong constraints from massive unlabeled data. More specifically, this paper presents a biased cross-window consistency (BCC) loss with an importance factor, which helps the deep network explicitly constrain confidence maps from overlapping regions in different windows to maintain semantic consistency with larger contexts. In addition, we propose a dynamic pseudo-label memory bank (DPM) to provide high-consistency and high-reliability pseudo-labels to further optimize the network. Extensive experiments on three representative datasets of urban views, medical scenarios, and satellite scenes with consistent performance gain demonstrate the superiority of our framework. Our code is released at https://jack-bo1220.github.io/project/CWC.html.