Transfer Learning methods are widely used in satellite image segmentation problems and improve performance upon classical supervised learning methods. In this study, we present a semantic segmentation method that allows us to make land cover maps by using transfer learning methods. We compare models trained in low-resolution images with insufficient data for the targeted region or zoom level. In order to boost performance on target data we experiment with models trained with unsupervised, semi-supervised and supervised transfer learning approaches, including satellite images from public datasets and other unlabeled sources. According to experimental results, transfer learning improves segmentation performance 3.4% MIoU (Mean Intersection over Union) in rural regions and 12.9% MIoU in urban regions. We observed that transfer learning is more effective when two datasets share a comparable zoom level and are labeled with identical rules; otherwise, semi-supervised learning is more effective by using the data as unlabeled. In addition, experiments showed that HRNet outperformed building segmentation approaches in multi-class segmentation.
A High Resolution Urban and Rural Settlement Map of Africa Using Deep Learning and Satellite Imagery
Institute for Analytical Sociology, Linköping University, Sweden（分析社会研究所，利尔贝里大学，瑞典）；Geomatics, Department of Environmental and Life Sciences, Karlstad University, Sweden（测绘学，环境与生命科学系，卡尔斯塔德大学，瑞典）；Department of Statistics, Harvard University, Massachusetts, USA（统计学系，哈佛大学，马萨诸塞州，美国）；Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden（计算机科学与工程系，查尔姆斯理工大学，哥德堡，瑞典）；The AI and Global Development Lab (www.global-lab.ai)（人工智能与全球发展实验室）
Benchmarking Deep Learning Models for Aerial LiDAR Point Cloud Semantic Segmentation under Real Acquisition Conditions: A Case Study in Navarre
Department of Statistics, Computer Science and Mathematics and Institute of Smart Cities (ISC), Public University of Navarre (UPNA)（统计、计算机科学与数学系和智能城市研究所（ISC），纳瓦拉公共大学（UPNA））；Tracasa Instrumental
FM-LC: A Hierarchical Framework for Urban Flood Mapping by Land Cover Identification Models
Department of Environmental Sciences, College of Natural and Health Sciences, Zayed University（环境科学系，自然与健康科学学院，扎耶德大学）；Computer Science department of Arizona State University（亚利桑那州立大学计算机科学系）
Joint Super-Resolution and Segmentation for 1-m Impervious Surface Area Mapping in China's Yangtze River Economic Belt
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China（中国科学院 aerospace information research institute, 北京）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China（中国科学院大学电子电气与通信工程学院, 北京）；School of Mathematics, Southeast University, Nanjing 210096, China（东南大学数学学院, 南京）；Graduate School of Frontier Sciences, the University of Tokyo, Chiba 277-8561, Japan（东京大学前沿科学研究生院, 日本）；RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan（RIKEN 高度智能项目中心, 日本）
DExTeR: Weakly Semi-Supervised Object Detection with Class and Instance Experts for Medical Imaging
University of Strasbourg, CNRS, INSERM, ICube, UMR7357, France（斯特拉斯堡大学、法国国家科学研究中心、法国国家医学研究院、ICube、UMR7357、法国）；University Hospital of Strasbourg, France（斯特拉斯堡大学医院、法国）
