Papers Building change detection for remote sensing images
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BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response
Disaster events occur around the world and cause significant damage to human life and property. Earth observation (EO) data enables rapid and comprehensive building damage assessment (BDA), an essential capability in the…
AllBuilding change detection for remote sensing imagesBuilding Damage AssessmentChange Detection+3SRC-Net: Bi-Temporal Spatial Relationship Concerned Network for Change Detection
Change detection (CD) in remote sensing imagery is a crucial task with applications in environmental monitoring, urban development, and disaster management. CD involves utilizing bi-temporal images to identify changes ov…
Building change detection for remote sensing imagesChange DetectionRS-Mamba for Large Remote Sensing Image Dense Prediction
Context modeling is critical for remote sensing image dense prediction tasks. Nowadays, the growing size of very-high-resolution (VHR) remote sensing images poses challenges in effectively modeling context. While transfo…
Building change detection for remote sensing imagesChange DetectionMambaPrediction+2MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
Foundation models have reshaped the landscape of Remote Sensing (RS) by enhancing various image interpretation tasks. Pretraining is an active research topic, encompassing supervised and self-supervised learning methods …
Aerial Scene ClassificationBuilding change detection for remote sensing imagesChange DetectionChange detection for remote sensing images+13MaskChanger: A Transformer-Based Model Tailoring Change Detection with Mask Classification
Change detection in multi-temporal remote sensing data enables crucial urban analysis and environmental monitoring applications. However, complex factors like illumination variance and occlusion make robust automated cha…
Building change detection for remote sensing imagesChange DetectionDecoderA New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection
Change detection (CD) is a critical task to observe and analyze dynamic processes of land cover. Although numerous deep learning-based CD models have performed excellently, their further performance improvements are cons…
Building change detection for remote sensing imagesChange DetectionGeneral KnowledgeLightCDNet: Lightweight Change Detection Network Based on VHR Images
Lightweight change detection models are essential for industrial applications and edge devices. Reducing the model size while maintaining high accuracy is a key challenge in developing lightweight change detection models…
Building change detection for remote sensing imagesChange DetectionDecoderUltralightweight Spatial–Spectral Feature Cooperation Network for Change Detection in Remote Sensing Images
Deep convolutional neural networks (CNNs) have achieved much success in remote sensing image change detection (CD) but still suffer from two main problems. First, the existing multiscale feature fusion methods often use …
Building change detection for remote sensing imagesChange DetectionChange detection for remote sensing imagesDetecting Building Changes with Off-Nadir Aerial Images
The tilted viewing nature of the off-nadir aerial images brings severe challenges to the building change detection (BCD) problem: the mismatch of the nearby buildings and the semantic ambiguity of the building facades. T…
Building change detection for remote sensing imagesChange DetectionTransition Is a Process: Pair-to-Video Change Detection Networks for Very High Resolution Remote Sensing Images
As an important yet challenging task in Earth observation, change detection (CD) is undergoing a technological revolution, given the broadening application of deep learning. Nevertheless, existing deep learning-based CD …
Building change detection for remote sensing imagesChange DetectionChange detection for remote sensing imagesEarth Observation+1SARAS-Net: Scale and Relation Aware Siamese Network for Change Detection
Change detection (CD) aims to find the difference between two images at different times and outputs a change map to represent whether the region has changed or not. To achieve a better result in generating the change map…
Building change detection for remote sensing imagesChange DetectionChange detection for remote sensing imagesRelation+1Changer: Feature Interaction is What You Need for Change Detection
Change detection is an important tool for long-term earth observation missions. It takes bi-temporal images as input and predicts "where" the change has occurred. Different from other dense prediction tasks, a meaningful…
Building change detection for remote sensing imagesChange DetectionEarth ObservationSiamixFormer: a fully-transformer Siamese network with temporal Fusion for accurate building detection and change detection in bi-temporal remote sensing images
Building detection and change detection using remote sensing images can help urban and rescue planning. Moreover, they can be used for building damage assessment after natural disasters. Currently, most of the existing m…
2D Semantic SegmentationBuilding change detection for remote sensing imagesBuilding Damage AssessmentChange Detection+3TINYCD: A (Not So) Deep Learning Model For Change Detection
In this paper, we present a lightweight and effective change detection model, called TinyCD. This model has been designed to be faster and smaller than current state-of-the-art change detection models due to industrial n…
Building change detection for remote sensing imagesChange DetectionChange detection for remote sensing imagesDeep LearningAn Empirical Study of Remote Sensing Pretraining
Deep learning has largely reshaped remote sensing (RS) research for aerial image understanding and made a great success. Nevertheless, most of the existing deep models are initialized with the ImageNet pretrained weights…
Aerial Scene ClassificationBuilding change detection for remote sensing imagesChange DetectionChange detection for remote sensing images+4Building Extraction from Remote Sensing Images with Sparse Token Transformers
Deep learning methods have achieved considerable progress in remote sensing image building extraction. Most building extraction methods are based on Convolutional Neural Networks (CNN). Recently, vision transformers have…
Building change detection for remote sensing imagesExtracting Buildings In Remote Sensing ImagesChange is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery
For high spatial resolution (HSR) remote sensing images, bitemporal supervised learning always dominates change detection using many pairwise labeled bitemporal images. However, it is very expensive and time-consuming to…
Building change detection for remote sensing imagesChange detection for remote sensing imagesSemantic SegmentationAdversarial Instance Augmentation for Building Change Detection in Remote Sensing Images
Training deep learning-based change detection (CD) models heavily relies on large labeled data sets. However, it is time-consuming and labor-intensive to collect large-scale bitemporal images that contain building change…
Building change detection for remote sensing imagesChange DetectionImage AugmentationRemote Sensing Image Change Detection with Transformers
Modern change detection (CD) has achieved remarkable success by the powerful discriminative ability of deep convolutions. However, high-resolution remote sensing CD remains challenging due to the complexity of objects in…
Building change detection for remote sensing imagesChange DetectionLooking for change? Roll the Dice and demand Attention
Change detection, i.e. identification per pixel of changes for some classes of interest from a set of bi-temporal co-registered images, is a fundamental task in the field of remote sensing. It remains challenging due to …
Building change detection for remote sensing imagesChange DetectionChange detection for remote sensing images