How to Crack Complex Geolocation Challenges
- Author: Martyna Marciniak
- Full Title: How to Crack Complex Geolocation Challenges: A Case Study of the Mahibere Dego Massacre
- Type: #snippet✂️
- Document Tags: #geospatial
- URL: https://citizenevidence.org/2021/04/09/geolocation-mahibere-dego/
Highlights
- This lingering doubt was caused by the inherent flat rendering of the Google Earth Pro mesh. While attempting to match the location of the footage, the rendering quality made it difficult to perceive depth of field and therefore made differentiating mountain ranges from each other more complicated. The greater uncertainty was heightened by the apparent lack of spatial representation of smaller features visible in the texture of the Google Earth Pro landscape – such as mountain peaks, cliff-edges or gullies. (View Highlight)
- In general, given these known limitations, seeking higher precision for the camera location is not necessary to establish the general location. For our geolocation purposes we needed to ensure this wasn’t caused by an error in the camera matching, as we set out to map the rest of the footage onto the landscape as accurately as possible and identify other features in the process. (View Highlight)
- In general, given these known limitations, seeking higher precision for the camera location is not necessary to establish the general location. For our geolocation purposes we needed to ensure this wasn’t caused by an error in the camera matching, as we set out to map the rest of the footage onto the landscape as accurately as possible and identify other features in the process. (View Highlight)
- Importantly, the reconstruction of the foreground allowed a more confident mapping of the second part of the footage in the model space. This further matched the features seen in the imagery background – the cliffside, which was another important component of the geolocation process. (View Highlight)
- Therefore we suspected that the footage could have been taken from this general area. Similarly to the processes described previously – we placed a tracked camera for a segment of the footage in this location. The background in the model matched the footage, yet given the distance to the matching objects we were conscious of possible error. (View Highlight)