In this study, an end-to-end Multi-Scale Geoscience Network (MS-GeoNet) is proposed for building footprint extraction. The proposed architecture focuses on multi-scale nested characteristics and the spatial correlation between buildings and surroundings. The performance of a number of embedding modules and loss ...

The precise building extraction from high-resolution remote sensing images holds significant application for urban planning, resource management, and environmental conservation. In recent years, deep neural networks (DNNs) have garnered substantial attention for their adeptness in learning and extracting features, ...

Multi-scale attention integrated hierarchical networks for high-resolution building footprint extraction. https://doi.org/10.1016/j.jag.2022.102768. Journal: International Journal of Applied Earth Observation and Geoinformation, 2022, p. 102768. Publisher: Elsevier BV. Authors: Tang Liu; Ling Yao; Jun Qin; Ning Lu; Hou ...

Feb 13, 2024 · In our approach, we initially extracted multi-scale building feature information, leveraging the multi-scale channel attention mechanism and multi-scale spatial attention mechanism. Subsequently, we employed adaptive hierarchical weighting processes on the extracted building features.

The extraction of building footprints, as a highly challenging task in remote sensing (RS) image-based geospatial object detection and recognition, holds significant importance. Due to the strong coupling in RS images between the body and boundary of buildings, the ability of most currently advanced deep learning ...

Parallel Attention Network for Building Extraction in High-Resolution Remote Sensing. Images. IEEE Transactions on Geoscience and Remote Sensing 59, 4287 ... Optimization and Multi-scale Context Awareness Based Building Extraction from. High-Resolution Remote Sensing Imagery. IEEE Transactions on Geoscience and.

The study [4] introduces a multiscale building extraction method using refined attention pyramid networks (RAPNets), integrating atrous and deformable convolutions, attention mechanisms, and pyramid pooling to enhance feature extraction and fusion, demonstrating superior performance on the Inria and xBD datasets. The ...

To solve these problems, we propose the multi-scale boundary-refined HRNet (MBR-HRNet) model, which preserves detailed boundary features for accurate building segmentation. The boundary refinement module (BRM) enhances the accuracy of small buildings and boundary extraction in the building segmentation network by ...

Missing: footprint | Show results with:footprint

Mar 2, 2024 · 5: Utilize LRCM modules to integrate and fuse features from various hierarchical levels. 6: Perform ... This study introduces a novel multi-feature fusion network (MFFNet), with the aim of enhancing the accuracy of building extraction from high-resolution remote sensing images of various ...