The segmentation algorithm for buildings and waters is extremely important for the efficient planning and utilization of land resources. The temporal and space range of remote sensing pictures is growing. Due to the generic convolutional neural network’s (CNN) insensitivity to the spatial position information in remote sensing images, certain location and edge details can be lost, leading to a low level of segmentation accuracy. This research suggests a double-branch parallel interactive network to address these issues, fully using the interactivity of global information in a Swin Transformer network, and integrating CNN to capture deeper information. Then, by building a cross-scale multi-level fusion module, the model can combine features gathered using convolutional neural networks with features derived using Swin Transformer, successfully extracting the semantic information of spatial information and context. Then, an up-sampling module for multi-scale fusion is suggested. It employs the output high-level feature information to direct the low-level feature information and recover the high-resolution pixel-level features. According to experimental results, the proposed networks maximizes the benefits of the two models and increases the precision of semantic segmentation of buildings and waters.

/pdf/double-branch-parallel-network-for-segmentation-of-buildings-2j0vgsdy.pdf

Double Branch Parallel Network for Segmentation of Buildings and Waters in Remote Sensing Images

pdf/rs-mamba-for-large-remote-sensing-image-dense-prediction-2qqdkakroe.pdf

RS-Mamba for Large Remote Sensing Image Dense Prediction

Deep learning (DL)-based methods have recently shown great promise in bitemporal change detection (CD). Existing discriminative methods based on convolutional neural networks (CNNs) and Transformers rely on discriminative representation learning for change recognition while struggling with exploring local and long-range contextual dependencies. As a result, it is still challenging to obtain fine-grained and robust CD maps in diverse ground scenes. To cope with this challenge, this work proposes a generative CD model called GCD-DDPM to directly generate CD maps by exploiting the denoising diffusion probabilistic model (DDPM), instead of classifying each pixel into changed or unchanged categories. Furthermore, the difference conditional encoder (DCE), is designed to guide the generation of CD maps by exploiting multilevel difference features. Leveraging the variational inference (VI) procedure, GCD-DDPM can adaptively recalibrate the CD results through an iterative inference process, while accurately distinguishing subtle and irregular changes in diverse scenes. Finally, a noise suppression-based semantic enhancer (NSSE) is specifically designed to mitigate noise in the current step’s change-aware feature representations from the CD Encoder. This refinement, serving as an attention map, can guide subsequent iterations while enhancing CD accuracy. Extensive experiments on four high-resolution CD datasets (CDD) confirm the superior performance of the proposed GCD-DDPM. The code for this work will be available at https://github.com/udrs/GCD.

GCD-DDPM: A Generative Change Detection Model Based on Difference-Feature-Guided DDPM

Abstract The study introduces an automated approach for extracting water bodies from satellite images using the Faster R-CNN algorithm. The approach was tested on two datasets consisting of water body images collected from Sentinel-2 and Landsat-8 (OLI) satellite images, totaling over 3500 images. The results showed that the proposed approach achieved an accuracy of 98.7% and 96.1% for the two datasets, respectively. This is significantly higher than the accuracy achieved by the convolutional neural network (CNN) approach, which achieved 96% and 80% for the two datasets, respectively. These findings highlight the effectiveness of the proposed approach in accurately mapping water bodies from satellite imagery. Additionally, the Sentinel-2 dataset performed better than the Landsat dataset in both the Faster R-CNN and CNN approaches for water body extraction. 

/pdf/deep-learning-for-automatic-extraction-of-water-bodies-using-1qv5tssi.pdf

Deep Learning for Automatic Extraction of Water Bodies Using Satellite Imagery

With the fast development of 3D data acquisition techniques, topographic point clouds have become easier to acquire and have promoted many geospatial applications. Ground filtering (GF), as one of the most fundamental and challenging tasks for the post-processing of large-scale topographic point clouds, has been extensively studied but has yet to be well solved. To reveal future superior solutions, a comprehensive investigation of up-to-date GF studies is essential. However, existing GF surveys are scarce and fail to capture the latest progress and advancements. To this end, this paper not only presents a comprehensive review of up-to-date and advanced GF methods, but also conducts systematic comparative analyses of existing experimental results on public GF benchmark datasets. Moreover, this survey compiles the most recent publicly available resources that can be leveraged for the GF research, including pertinent datasets, metrics, and a range of open-source tools. Finally, the remaining challenges and promising research directions of GF, as well as implications for large-scale 3D geospatial understanding, are discussed in-depth. It is expected that this survey can simultaneously serve as a position paper and tutorial to those interested in GF. 

Towards intelligent ground filtering of large-scale topographic point clouds: A comprehensive survey

An attention-based multiscale transformer network for remote sensing image change detection

MVPNet: A multi-scale voxel-point adaptive fusion network for point cloud semantic segmentation in urban scenes

Water is a kind of vital natural resource, which acts as the lifeblood of the ecosystem and the energy source for the living and production activities of humans. Regularly mapping the conditions of water resources and taking effective measures to prevent them from pollutions and shortages are very important and necessary to maintain the sustainability of the ecosystem. As a preliminary step for image-based water resource analysis, the complete recognition and accurate extraction of water bodies are important prerequisites in many applications. Nevertheless, due to the issues of topology diversities, appearance variabilities, and land cover interferences, there is still a large gap to achieve the human-level water bodies interpretation quality. This paper presents a hierarchical attentive high-resolution network, abbreviated as WaterHRNet, for extracting water bodies from remote sensing imagery. First, by building a multibranch high-resolution feature extractor integrated with global feature semantics aggregation, the WaterHRNet behaves laudably to supply high-quality, strong-semantic feature representations. Furthermore, by inlaying an effective feature attention scheme with the comprehensive exploitation of both the spatial and channel feature significances, the WaterHRNet is forced to strengthen the semantic-determinate, task-aware feature encodings. In addition, by designing a hierarchical processing principle with the progressive enhancement of category-attentive feature semantics, the WaterHRNet performs effectively to export semantic-discriminative, target-oriented feature representations for precise water body segmentation. The WaterHRNet is elaborately verified both quantitatively and qualitatively on three remote sensing datasets. Evaluation results show that the WaterHRNet achieves an average precision of 98.44%, average recall of 97.84%, average IoU of 96.35%, and average F1-score of 98.14%. Comparative analyses also demonstrate the superior performance and excellent feasibility of the WaterHRNet in segmenting water bodies. 

WaterHRNet: A multibranch hierarchical attentive network for water body extraction with remote sensing images

The capability of partially penetrating vegetation canopy and efficiently collecting high-precision point clouds over large areas makes airborne laser scanning (ALS) a valuable tool for various geospatial applications. However, automated ground filtering (GF), one fundamental and challenging step for most ALS applications, has remained a widely researched yet unsolved problem for decades. The recent breakthroughs in supervised deep learning (DL) techniques, which rely on sufficient and high-quality labeled datasets, provide a new solution to better solve this problem. Unfortunately, public 3D geospatial datasets are scarce, especially for those tailored for the landform-scale GF task. Moreover, whether advanced deep neural networks (DNNs) can be well-scaled to the problem of GF remains an open question. To comprehensively advance the development of effective DL-based GF pipelines, we first publish an ultra-large-scale GF dataset built upon open-access ALS point clouds of four different countries worldwide, which covers over 47 km2 and nine different terrain scenes. Then, multiple attractive advantages of DL techniques in GF are evaluated through extensive experimental comparisons with traditional GF methods on the presented dataset. Furthermore, we reveal several issues faced by generalizing existing advanced 3D DNNs into GF tasks with a series of in-depth experimental analyses. Finally, some promising directions for future research are suggested in response to the identified challenges. Our dataset, named OpenGF, is available at https://github.com/Nathan-UW/OpenGF. 

Deep learning for filtering the ground from ALS point clouds: A dataset, evaluations and issues

Traffic signs are indispensable fixtures in modern transportation activities, which are installed along the roadside or over the pathway to provide important prompt messages. The timely emergences, complete visibilities, and definite signals of traffic signs matter significantly to direct the driving behaviors and ensure the convenience and security of the transportation activities. Moreover, the efficient and accurate recognition of traffic signs can also provide decisive evidences to a variety of intelligent transportation systems. Nevertheless, the unignorable presence issues of traffic signs caused by vision factors, such as small sizes, dimness, deformations, and occlusions, impact essentially on the high-quality recognition of traffic signs. This paper develops a novel attentive semi-anchoring guided high-resolution network, named SignHRNet, for street-level traffic signs recognition purpose. First, stacked with a high-resolution network boosted by a dual-attention module as the feature extractor, the SignHRNet can exploit informative channel features and task-oriented spatial features to generate multiscale strong feature semantics for instance-level predictions. Second, designed with a semi-anchoring guided detection strategy assisted by instance-aware feature alignment, the SignHRNet can achieve highly-efficient sign type categorization and highly-accurate sign location determination. The proposed SignHRNet is intensively evaluated on three large-size datasets towards traffic signs recognition. Quantitative verifications demonstrate a competitive accuracy with an average mAP, mAP50, and mAP75 of 72.85%, 96.48%, and 85.31%, respectively, in processing traffic signs of varying self-conditions under diverse scenarios. Ablative and comparative analyses also confirmed the practical reliability and performance superiority of the SignHRNet in traffic signs recognition applications. 

SignHRNet: Street-level traffic signs recognition with an attentive semi-anchoring guided high-resolution network

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