Learning to Extract Building Footprints from Off-Nadir Aerial Images
Wuhan University（武汉大学）；SenseTime Research（商汤科技研究院）；University of Electronic Science and Technology of China（电子科技大学）；Sun Yat-Sen University（中山大学）；CUHK-SenseTime joint Lab（香港中文大学-商汤科技联合实验室）
Extracting building footprints from aerial images is essential for precise urban mapping with photogrammetric computer vision technologies. Existing approaches mainly assume that the roof and footprint of a building are well overlapped, which may not hold in off-nadir aerial images as there is often a big offset between them. In this paper, we propose an offset vector learning scheme, which turns the building footprint extraction problem in off-nadir images into an instance-level joint prediction problem of the building roof and its corresponding "roof to footprint" offset vector. Thus the footprint can be estimated by translating the predicted roof mask according to the predicted offset vector. We further propose a simple but effective feature-level offset augmentation module, which can significantly refine the offset vector prediction by introducing little extra cost. Moreover, a new dataset, Buildings in Off-Nadir Aerial Images (BONAI), is created and released in this paper. It contains 268,958 building instances across 3,300 aerial images with fully annotated instance-level roof, footprint, and corresponding offset vector for each building. Experiments on the BONAI dataset demonstrate that our method achieves the state-of-the-art, outperforming other competitors by 3.37 to 7.39 points in F1-score. The codes, datasets, and trained models are available at https://github.com/jwwangchn/BONAI.git.
LoDEOT: Low-Dimensional and Efficient Offset Tokens for Building Footprint Extraction from Off-Nadir Imagery
City University of Hong Kong（香港城市大学）；University of the Chinese Academy of Sciences（中国科学院大学）；University of Hong Kong（香港大学）；Zhejiang University（浙江大学）；University of Southampton（南安普顿大学）
DragOSM: Extract Building Roofs and Footprints from Aerial Images by Aligning Historical Labels
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；Department of Data Science, City University of Hong Kong（香港城市大学数据科学系）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空航天信息研究所）
PolyFootNet: Extracting Polygonal Building Footprints in Off-Nadir Remote Sensing Images
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空信息研究所）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；Applied Machine Learning Lab, School of Data Science, City University of Hong Kong（香港城市大学数据科学学院应用机器学习实验室）
从高分辨率航空影像中提取建筑物足迹一直是城市动态监测、规划和管理的重要组成部分。这也是遥感研究中的一项具有挑战性的任务。近年来，深度神经网络在提高遥感影像建筑物提取精度方面取得了很大的成就。然而，现有的大多数方法通常需要大量的参数和浮点运算才能达到高精度，这导致了高内存消耗和低推理速度，不利于研究。在本文中，我们提出了一种新的高效网络命名为ESFNet，它采用可分离因子分解的残差块，并利用扩张卷积，旨在保持轻微的精度损失，低计算成本和内存消耗。我们的ESFNet在准确性和效率之间获得了更好的权衡，它可以在单个Tesla V100上以超过100 FPS的速度运行，所需的FLOP比最先进的实时架构ERFNet少6倍，参数少18倍，同时保持类似的准确性，而无需任何额外的上下文模块，后处理和预训练方案。我们在WHU Building Dataset上评估了我们的网络，并将其与其他最先进的架构进行了比较。综合分析表明，该网络有利于有效的遥感研究，并可进一步推广到其他领域。该代码可在https://github.com/mrluin/ESFNet-Pytorch上公开获取
MTBF-33: A multi-temporal building footprint dataset for 33 counties in the United States (1900-2015)
University of Colorado Boulder（科罗拉多大学博尔德分校）；Cooperative Institute for Research in Environmental Sciences (CIRES)（环境科学合作研究所（CIRES））；Institute of Behavioral Science（行为科学研究所）
FLAME 3 Dataset: Unleashing the Power of Radiometric Thermal UAV Imagery for Wildfire Management
Holcombe Department of Electrical and Computer Engineering, Clemson University（克莱姆森大学霍尔科姆电气与计算机工程系）；Pacific Southwest Research Station, U.S. Forest Services（美国林业局太平洋西南研究站）；School of Environmental and Forest Sciences, University of Washington（华盛顿大学环境与森林科学学院）；US Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory（美国林业局落基山研究站火灾科学实验室）；Department of Mechanical Engineering, Clemson University（克莱姆森大学机械工程系）；Department of Forest Management, University of Montana（蒙大拿大学森林管理系）
Prompt-Driven Building Footprint Extraction in Aerial Images with Offset-Building Model
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；City University of Hong Kong（香港城市大学）
