Analyzing Decades-Long Environmental Changes in Namibia Using Archival
  Aerial Photography and Deep Learning
Girmaw Abebe Tadesse Caleb Robinson Gilles Quentin Hacheme Akram Zaytar Rahul Dodhia Tsering Wangyal Shawa Juan M. Lavista Ferres Emmanuel H. Kreike
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
class-weighting, pseudo-labeling and empirical p-value-based filtering to
balance skewed and sparse representations of objects in the ground truth data.
Results demonstrate the benefits of these different training strategies
resulting in an average $F_1=0.661$ and $F_1=0.755$ over the three objects of
interest for the 1943 and 1972 imagery, respectively. We also identified that
the average size of Waterhole and Big trees increased while the average size of
Omutis decreased between 1943 and 1972 reflecting some of the local effects of
the massive post-Second World War economic, agricultural, demographic, and
environmental changes. This work also highlights the untapped potential of
historical aerial photographs in understanding long-term environmental changes
beyond Namibia (and Africa). With the lack of adequate satellite technology in
the past, archival aerial photography offers a great alternative to uncover
decades-long environmental changes.