Room: Auditorium B
Academic Track 🎓
Duration: 5 minutes (plus Q&A)
This pilot project is connected to a larger initiative to open-source the assisted mapping platform for Humanitarian OpenStreetMap (HOTOSM) based on Very High Resolution (VHR) drone imagery. The study test and evaluate multiple U-Net based architectures on building segmentation of Refugee Camps in East Africa.
Refugee camps and informal settlements provide accomodation to some of the most vulnerable population, the majority of which are located in Sub- Saharan East Africa (UNHCR, 2016). Many of these settlements often lack up-to-date maps of which we take for granted in developed settlements. Hav- ing up-to-date maps are important for assisting administration tasks such as population estimates and infrastructure development in data impoverished environments, and thereby encourages economic productivity (Herfort et al., 2021). The data inequality between the developed and developing countries are often resulted from a lack of commercial interest, especially with the recent trend of corporate OSM mappers (Anderson et al., 2019, Veselovsky et al., 2021). Such disparity can be reduced using assisted mapping tech- nology. To extract geospatial and imagery characteristics of dense urban enviornments, a combination of VHR satellite imagery and Machine Learn- ing (ML) are commonly used (Taubenböck et al., 2018). Classical ML based methods that exploit the textual (e.g. GLCM), spectral, and morphological characteristics of VHR imagery are based on the principles of Computer Vision (CV). Although many have shown promising results in satellite VHR (1m to 5m resolution) scenarios such as differentiating slum and non-slum (Kuffer et al., 2016 & Wurm et al., 2021), in VHR drone imagery (5cm to 10cm resolution) however, results might suffer from noise caused by environ- ment and drone-based specific problems such as motion artefacts and litter. Recent advances in CV based Deep Learning might be able to address these issues (Chen et al., 2021 & Carrivick et al., 2016).
Purpose of the study
The study is connected to a larger initiative to open-source the assisted mapping platform in the current Humanitarian OpenStreetMap (HOTOSM) ecosystem. This study is a pilot-project to investigate the capabilities of applying semantic segmentation using community open-sourced VHR drone imagery collected by the partner organisation OpenAerialMap. The study aims to rigourosly assess the various components and inputs that would contribute to the ML based mapping system, and to produce a detailed evaluation on class-based accuracy assessment (Congalton & Green, 2019). This pilot study focuses on 2 camps in East Africa, where data availability and the geography of the camps are within a similar savannah ecosystem. This enables highly-detailed method testing and analysis of transferability of the results between the two camps.
Data and Methodology
The first camp is located in Dzaleka, Dowa, Malawi, which is sub-divided into the Dzaleka North and Dzaleka main camp. The Dzaleka camps are home to around 40,000 refugees mainly coming from the African Great Lakes region. The Dzaleka North camp is characterised by a newer, spatially well- planned metal-sheeted roofs, while the southern main camp is characterised by complex, dense mud-walled building with stone-lined thatched-roofs (UN- HCR, 2014). The second camp, the Kalobeyei settlement is part of the sub- camp of Kakuma, located in the rural county of Turkana, North-West Kenya. The Kalobeyei settlement was home to approximately 34,849 refugees as of
Since CV based Deep Learning is very dependent on the quality of the labelled referenced data, especially when performing pixel-based semantic segmentaion, it is of crucial importance that care is taken when producing highly accurate labels that ensure sucessful training (Ng A., 2018). A large quanitiy of available labels did not have such a task in mind, imperfection in labelling around existing drone artefacts could cause the trained model to misclassify such pixels. In order to train a model which performs well on drone imagery, the motion artefact will be a signficant feature for the model to learn.he combination of data availability have allow a unique set of research questions concerning the input data quality and experiment setup to surface. Therefore, to test out U-Net and a few variation of the U-Net performance, an additional set of label data was created in order to supple- ment the imperfection in the labelled data of the Dzaleka camps. Initially, the models will be trained on the pixel-perfect and less complex Kalobeyei dataset, this will be then be followed by introducing the Dzaleka datasets of higher complexity. A comparison of baseline performance between the U- Net variations (Ronneberger et al., 2015) and the Open-Cities-AI-Challenge (OCC) winning model is conducted. The baseline experiement aims to keep the hyperparameters (e.g. optimiser, learning rate, weight decay etc.) con- stant to obtain an objective view of the architectual responses on the same dataset setup. This will provide a clear picture of the feasibility and how to take this project further, so that further resources could be justified to scale future experiments.
Findings and Discussion
Initial baseline experiments on the Kalobeyei dataset and Kalobeyei with the Dzaleka(s) seem to suggest limited transferability from the OCC model. This suggests that the OCC model is perhaps over-generalised to the compe- tition test dataset. Despite achieving very high confidence on metal-sheeted rooftops, it does not detect any of the more complicated thatched roofs com- mon in the Dzaleka camp. The OCC model also struggle with some of the more obscure drone motion artefacts occuring at the edge of the imagery in the Kalobeyei camp. Meanwhile, the Precision and Recall statis- tics favour other variations or further transfer training on the OCC model, and the EfficientNet B1 header U-Net pretrained with ImageNet weights. However validation loss suggests there might be little room for improvement in the further transfer training of the OCC model.
Precision and Recall have both reached above 0.7 in most experiments, which outline the general capability of the strategies used. However there are still significant variations among different architectures and setups. The next step is to perform systemic fine-tuning to increase the confidence level of the appropriate architectures.