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 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.
Abstract (translated)
本研究旨在探讨纳米比亚历史航空照片中的目标检测，以识别长期环境变化。具体来说，我们将利用1943年和1972年的亚米空间灰度级下视影像，在奥西康戈（Oshikango）地区识别关键物体——\textit{水坑}、\textit{奥穆蒂家园}和\textit{大樹}。在这项工作中，我们提出了一个利用深度语义分割模型分析历史航空照片的工作流程。为此，我们采用了一些策略，包括分类加权、伪标签和基于实证p值的过滤，以平衡真实数据中物体地面真实表示的偏态和稀疏性。结果表明，这些不同的训练策略带来了平均F1值分别为0.661和0.755，分别针对1943年和1972年的感兴趣对象。我们还发现，水坑和的大树的平均尺寸在1943年到1972年之间增加，而奥穆蒂家园的平均尺寸减少，反映了二战后大规模经济、农业、人口和环境变化的一些局部影响。这项工作还强调了历史航空照片在纳米比亚（非洲）以外地区理解长期环境变化（ beyond Namibia and Africa）的未开发潜力。由于过去的卫星技术不足，档案馆航空摄影提供了揭示几十年环境变化的大好途径。
URL
https://arxiv.org/abs/2404.08544
