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

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

Jihyeon Janel Lee
Joseph Z. Xu
Kihyuk Sohn
W. Lu
David Berthelot
Izzeddin Gur
Pranav Khaitan
Ke-Wei
Ke Huang
Kyriacos M. Koupparis
Bernhard Kowatsch
ArXiv (abs)PDFHTML

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

4 / 4 papers shown
Title
Automated Wildfire Damage Assessment from Multi view Ground level Imagery Via Vision Language Models
Automated Wildfire Damage Assessment from Multi view Ground level Imagery Via Vision Language Models
Miguel Esparza
Archit Gupta
Ali Mostafavi
Kai Yin
Yiming Xiao
12
0
0
02 Sep 2025
An Open-Source Tool for Mapping War Destruction at Scale in Ukraine using Sentinel-1 Time Series
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
219
9
0
21 Feb 2025
Disaster mapping from satellites: damage detection with crowdsourced point labels
Disaster mapping from satellites: damage detection with crowdsourced point labels
Danil Kuzin
Olga Isupova
Brooke D. Simmons
S. Reece
66
8
0
05 Nov 2021
Building Damage Mapping with Self-PositiveUnlabeled Learning
Building Damage Mapping with Self-PositiveUnlabeled Learning
J. Xia
Xiangwei Zhu
B. Adriano
63
4
0
04 Nov 2021

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.