We propose a segment anything model for building extraction (BuildingSAM) as a general solution for building extraction (BE) from high-resolution remote-sensing images. Unlike previous methods, BuildingSAM is constructed based on the Segment Anything Model (SAM) for large-scale images and parameter-efficient fine-tuning (PEFT), representing a novel research paradigm for BE. Although transformer-based architecture excels at processing global and low-frequency information, it can overlook local details and introduce biases in feature learning. To address these shortcomings, BuildingSAM incorporates a dual-branch module, in which one branch employs the image encoder of the lightweight next-generation semantic segmentation network SegNeXt to focus on capturing local building details, and the other uses a vision transformer (ViT) image encoder to extract global features. Furthermore, we introduce the Conv-LoRA method, which integrates ultra-lightweight convolutional parameters into low-rank adaptation (LoRA) to inject image-related inductive biases into the ViT image encoder. This approach enhances the ability of BuildingSAM to learn building boundary features in complex regions efficiently. In experiments conducted using two BE benchmark datasets, the proposed method significantly outperformed existing state-of-the-art BE models. Comprehensive ablation studies further validated the superior performance of BuildingSAM, at minimal additional computational cost.

We propose road-segment anything model (SAM), a universal model for extracting roads from large, very-high-resolution (VHR), optical, remote sensing (RS) images. Unlike previous methods, Road-SAM builds upon the foundation of the SAM, a large-scale image-segmentation model, to explore a new paradigm for customizable road extraction (RE). Within the framework, we introduce three variants that allow for flexible insertion of adapters at different positions within the transformer block. Additionally, the model employs a task-specific input module of explicit visual prompting (EVP) during training that uses embedded features and high-frequency component (HFC) information as prompts. Road-SAM also utilizes a carefully designed frequency adapter fine-tuning mechanism, leveraging lightweight yet effective fine-tuning techniques to integrate domain-specific RS knowledge into the RE model, enhancing segmentation performance and making efficient use of computational resources. Comprehensive experiments on two sets of RE benchmark datasets demonstrate the effectiveness of the proposed method. Extensive ablation experiments further validate its superiority over multiple state-of-the-art (SOTA) RS RE algorithms, with updates applied to only 10% of the parameters.

Vehicle re-identification supports cross-camera tracking and the location of specific vehicles in a smart city. The gallery images of vehicles are ranked based on the similarities in the appearance of objects to a vehicle query image. Previous work on vehicle re-identification has mainly focused on global or local analyses of predefined regions of vehicles to classify the vehicle images with a softmax loss function. On the one hand, separate global or predefined local regions of vehicles are often sensitive to perspective and occlusions. On the other hand, the embedding space supervised by the softmax loss function is not sufficiently compact for the object class. To solve these problems, we propose an end-to-end distance-based global and partial multi-regional deep network (DGPM) that combines multi-regional features to identify global and local differences. We exploit a three-branch architecture to learn the global and partial features from coarsely partitioned regions. A global similarity module is introduced to reduce the background information interference in the local branches. Unlike general classification, we design a distance-based classification layer that maintains consistency among criteria for similarity evaluation. Furthermore, we use spatiotemporal vehicle information to improve the vehicle re-identification results when the camera and shooting time are available. Systematic comparative evaluations performed on the large-scale VeRi and VehicleID datasets showed that our approach robustly achieved state-of-the-art performance. For instance, for the VeRi dataset, we achieve (79.39 + 2.78)% mAP and (96.19 + 2.26)% Rank-1 accuracy.

Fully convolutional network (FCN) modeling is a recently developed technique that is capable of significantly enhancing building extraction accuracy; it is an important branch of deep learning and uses advanced state-of-the-art techniques, especially with regard to building segmentation. In this paper, we present an enhanced deep convolutional encoder-decoder (DCED) network that has been customized for building extraction through the application of superpixel-based conditional random fields (SCRFs). The improved DCED network, with symmetrical dense-shortcut connection structures, is employed to establish the encoders for automatic extraction of building features. Our network's encoders and decoders are also symmetrical. To further reduce the occurrence of falsely segmented buildings, and to sharpen the buildings' boundaries, an SCRF is added to the end of the improved DCED architecture. Experimental results indicate that the proposed approach exhibits competitive quantitative and qualitative performance, effectively alleviating the salt-and-pepper phenomenon and retaining the edge structures of buildings. Compared with other state-of-the-art methods, our method demonstrably achieves the optimal final accuracies.

Water body extraction (WBE) has attracted considerable attention in the field of remote sensing image analysis. Herein, we present an enhanced deep convolutional encoder-decoder (DCED) network (or Deep U-Net) specifically tailored to WBE from remote sensing images by applying superpixel segmentation and conditional random fields (CRFs). First, we preclassify the entire remote sensing image into the water and nonwater areas via Deep U-Net, using the results of class membership probabilities as the unary potential in the CRF model. The pairwise potential of CRF is defined by a linear combination of Gaussian kernels, which forms a fully connected neighbor structure. Next, regional restriction is incorporated into the approach to enhance the consistency of the connected area. We use the simple linear iterative clustering algorithm to generate superpixels and correct the binary classification results by calculating their average posterior probabilities. Finally, a highly efficient approximate inference algorithm, mean-field inference, is generated for the final model. The results from the experimental application to GaoFen-2 images and WorldView-2 images demonstrate that the proposed approach exhibits competitive quantitative and qualitative performance, which effectively reduces salt-and-pepper noise and retains the edge structures of water bodies. Compared to existing state-of-the-art methods, our proposed method achieves superior final results.

This letter presents a novel flood detection approach using full polarimetric synthetic aperture radar (PolSAR) images based on a level set method considering prior geoinformation. The prior geoinformation includes information derived from vector data and topography data. The main approach accomplishes flood detection by the improved level set method, an active contour segmentation model, based on the classical Wishart distribution. Vector data are used to generate the zero initial level set curves. To investigate the separability between water and nonwater low-backscattering objects in PolSAR images, topography information is incorporated into the level set function as a constraint. Moreover, we introduce a piecewise statistical method to refine the result with the Kullback-Leibler divergence of circular polarization coherence. In addition, we design a new quantitative evaluation index to assess flood detection results. For validation, three real PolSAR images of flooded area are tested. The experimental results confirm the effectiveness of the proposed method.

The classification of damaged building types has received increasing attention in recent years. The detection of damaged rooftop areas is crucial to improve the accuracy of classification of building damaged types. In this letter, an approach for the automatic detection of damaged rooftops areas based on the visual bag-of-words (BoWs) model is presented. First, the building rooftop is segmented into different superpixel areas. Then, the visual BoWs model is employed to build semantic feature vectors for damaged or nondamaged parts of each superpixel area. Finally, damaged and nondamaged parts of rooftop superpixel areas are discriminated using support vector machine. An evaluation of experimental results, for a selected study site of the Beichuan earthquake ruins, Sichuan, China, shows that this method is feasible and effective for the detection of damaged rooftop areas.