Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques

24 November 2020

Papers citing "Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques"

6 / 6 papers shown

Title
An Open-Source Tool for Mapping War Destruction at Scale in Ukraine using Sentinel-1 Time Series Olivier Dietrich T. Peters Vivien Sainte Fare Garnot Valerie Sticher Thao T-T Whelan Konrad Schindler Jan Dirk Wegner 101 2 0 21 Feb 2025
CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery Thomas Manzini Priyankari Perali Raisa Karnik Robin Murphy 43 3 0 24 Jul 2024
Towards Efficient Disaster Response via Cost-effective Unbiased Class Rate Estimation through Neyman Allocation Stratified Sampling Active Learning Yanbing Bai Xinyi Wu Lai Xu Jihan Pei Erick Mas Shunichi Koshimura 36 1 0 28 May 2024
Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Data Tarun Kalluri Jihyeon Janel Lee Kihyuk Sohn Sahil Singla Manmohan Chandraker Joseph Z. Xu Jeremiah Liu 49 1 0 22 May 2024
Disaster mapping from satellites: damage detection with crowdsourced point labels Danil Kuzin Olga Isupova Brooke D. Simmons S. Reece 29 8 0 05 Nov 2021
Building Damage Mapping with Self-PositiveUnlabeled Learning J. Xia Naoto Yokoya B. Adriano 19 3 0 04 Nov 2021