Extracting Buildings In Remote Sensing Images
7 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.
