Analyzing Decades-Long Environmental Changes in Namibia Using Archival Aerial Photography and Deep Learning
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910416901242880 |
|---|---|
| author | Tadesse, Girmaw Abebe Robinson, Caleb Hacheme, Gilles Quentin Zaytar, Akram Dodhia, Rahul Shawa, Tsering Wangyal Ferres, Juan M. Lavista Kreike, Emmanuel H. |
| author_facet | Tadesse, Girmaw Abebe Robinson, Caleb Hacheme, Gilles Quentin Zaytar, Akram Dodhia, Rahul Shawa, Tsering Wangyal Ferres, Juan M. Lavista Kreike, Emmanuel H. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08544 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Analyzing Decades-Long Environmental Changes in Namibia Using Archival Aerial Photography and Deep Learning Tadesse, Girmaw Abebe Robinson, Caleb Hacheme, Gilles Quentin Zaytar, Akram Dodhia, Rahul Shawa, Tsering Wangyal Ferres, Juan M. Lavista Kreike, Emmanuel H. Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Analyzing Decades-Long Environmental Changes in Namibia Using Archival Aerial Photography and Deep Learning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2404.08544 |