Analyzing Decades-Long Environmental Changes in Namibia Using Archival Aerial Photography and Deep Learning

arXiv:2404.08544v1 Announce Type: new
Abstract: This study explores object detection in historical aerial photographs of Namibia to identify long-term environmental changes. Specifically, we aim to identify key objects -- \textit{Waterholes}, \textit{Omuti homesteads}, and \textit{Big trees} -- around Oshikango in Namibia using sub-meter gray-scale aerial imagery from 1943 and 1972. In this work, we propose a workflow for analyzing historical aerial imagery using a deep semantic segmentation model on sparse hand-labels. To this end, we employ a number of strategies including …

abstract aerial aim arxiv big cs.ai cs.cv deep learning detection environmental identify key long-term object objects photographs photography scale study trees type