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.

该研究分析历史航空照片中纳米比亚的目标物体检测，以识别长期环境变化，通过使用1943年和1972年的亚米尼亚次米级灰度航空图像，旨在识别纳米比亚Oshikango周围的关键对象，如Waterholes，Omuti homesteads和Big trees。结果表明，在使用深度语义分割模型进行分析的历史航空图像的训练策略中，多种策略包括类别加权，伪标记和经验p值过滤被采用，以平衡地面真实数据中物体的倾斜和稀疏表示，1943年和1972年图像的三个对象的平均F1值分别为0.661和0.755。这项工作还发现了水坑和大树的平均大小增加，而Omutis的平均大小在1943年至1972年间减小，反映了二战后经济，农业，人口和环境变化的地方效果。该研究也强调了历史航空照片在了解纳米比亚以外长期环境变化方面的潜力，由于过去缺乏足够的卫星技术，存档的航空摄影是揭示几十年环境变化的绝佳替代方法。

利用档案航空摄影和深度学习分析纳米比亚几十年来的环境变化