Extracting Buildings In Remote Sensing Images
10 papers with code • 3 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Extracting Buildings In Remote Sensing Images
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xBD
WHU Building Dataset
Massachusetts building dataset
MapAI Dataset
Most implemented papers
BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images
ShaneShen/BDANet-Building-Damage-Assessment
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With a pair of pre- and post-disaster satellite images, building damage assessment aims at predicting the extent of damage to buildings.
Building Extraction from Remote Sensing Images with Sparse Token Transformers
KyanChen/STT
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Remote Sens. 2021
Deep learning methods have achieved considerable progress in remote sensing image building extraction.
HEAT: Holistic Edge Attention Transformer for Structured Reconstruction
This paper presents a novel attention-based neural network for structured reconstruction, which takes a 2D raster image as an input and reconstructs a planar graph depicting an underlying geometric structure.
SDSC-UNet: Dual Skip Connection ViT-based U-shaped Model for Building Extraction
stdcoutzrh/BuildingExtraction
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IEEE Geoscience and Remote Sensing Letters 2023
Furthermore, unlike the previous single-skip-connection structure of U-shaped methods, we build a novel dual skip connection structure inside the model.
Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network
henryjiepanli/uncertainty-aware-network
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Building extraction aims to segment building pixels from remote sensing images and plays an essential role in many applications, such as city planning and urban dynamic monitoring.
DSAT-Net: Dual Spatial Attention Transformer for Building Extraction from Aerial Images
stdcoutzrh/BuildingExtraction
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IEEE Geoscience and Remote Sensing Letters 2023
The local attention path (LAP) uses efficient stripe convolution to generate local attention, which can alleviate the loss of information caused by down-sampling operation in the GAP and supplement the spatial details.
Learning Efficient Unsupervised Satellite Image-based Building Damage Detection
fzmi/ubdd
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Existing Building Damage Detection (BDD) methods always require labour-intensive pixel-level annotations of buildings and their conditions, hence largely limiting their applications.
Towards Automating the Retrospective Generation of BIM Models: A Unified Framework for 3D Semantic Reconstruction of the Built Environment
Semanticity-Research/SRBIM
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The adoption of Building Information Modeling (BIM) is beneficial in construction projects.
PolyR-CNN: R-CNN for end-to-end polygonal building outline extraction
PolyR-CNN demonstrates the capacity to deal with buildings with holes through a simple post-processing method on the Inria dataset.
Pix2Poly: A Sequence Prediction Method for End-to-end Polygonal Building Footprint Extraction from Remote Sensing Imagery
Extraction of building footprint polygons from remotely sensed data is essential for several urban understanding tasks such as reconstruction, navigation, and mapping.
