Papers with Code 아카이브
Extracting Buildings In Remote Sensing Images
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
PolyR-CNN: R-CNN for end-to-end polygonal building outline extraction
Towards Automating the Retrospective Generation of BIM Models: A Unified Framework for 3D Semantic Reconstruction of the Built Environment
Learning Efficient Unsupervised Satellite Image-based Building Damage Detection
DSAT-Net: Dual Spatial Attention Transformer for Building Extraction from Aerial Images
Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network
SDSC-UNet: Dual Skip Connection ViT-based U-shaped Model for Building Extraction
SiamixFormer: a fully-transformer Siamese network with temporal Fusion for accurate building detection and change detection in bi-temporal remote sensing images
Dual-Tasks Siamese Transformer Framework for Building Damage Assessment
HEAT: Holistic Edge Attention Transformer for Structured Reconstruction
BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images
Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model
GeoSay: A Geometric Saliency for Extracting Buildings in Remote Sensing Images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.