The detailed spatial data obtained from Unmanned Aerial Vehicles can shed light on subtle changes in informal settlements which may be linked to important socio-economic developments in the area. For example: the creation of new roads, building extensions, and incremental improvements in roof quality. Typical challenges for change detection methods include the detection of irrelevant changes through unsupervised methods, and training data requirements for supervised change detection methods. We therefore illustrate a workflow which combines rule-based, deep learning approach, and unsupervised change detection algorithms to develop an automatic workflow which identifies changes in informal settlements and assigns semantic meaning to them Figure 1: RGB imagery of an informal settlement in Rwanda (left column), rule-based class predictions (middle column) and predicted class label (right column) for UAV data from 2015 (top row) and 2017 (bottom row). Red indicates buildings, beige indicates terrain, green indicates trees, and white indicates low vegetation.
|Number of pages||1|
|Publication status||Published - 29 Nov 2018|
|Event||NCG symposium 2018 - Wageningen university, Wageningen, Netherlands|
Duration: 29 Nov 2018 → 29 Nov 2018
|Conference||NCG symposium 2018|
|Period||29/11/18 → 29/11/18|