  This study explores object detection in historical aerial photographs of
Namibia to identify long-term environmental changes. Specifically, we aim to
identify key objects -- Waterholes, Omuti homesteads, and 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 Omuti homesteads
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.
