Potential supervisory team: Stefan Lang, Dirk Tiede, Lorenz Wendt, Getachew Gella

Globally there is a profound number of the global population displaced from their homes because of natural and anthropogenic reasons. These forcibly displaced segment of the global population resides either in IDP sites or refugee camps if they cross international borders. Dwelling extraction and camp monitoring for a specific single camp can be efficiently done by using deep learning models and high-resolution satellite images and annotated labels to train the model. The biggest problem is when new IDPs/refugee camps are happening and rapid information retrieval is needed for emergency response for humanitarian response. The proposed work intends to leverage the learned skills of the model trained in IDP/refugee camps to extract features in completely unseen images taken from other new IDP/refugee camps. One of the constraints which limit this learning process is that dwelling characteristics in different refugee camps would be completely different which is governed by the medium of the material used to construct the houses, its contrast with the surrounding information, sizes of houses, and spacing of houses (See Fig. 1). A model trained in one site is said to be universally capable of extracting features if and only if it is spatially transferable and able to work very well in refugee camps that are located in other geographic locations. Therefore, the objective of the proposed work will be firstly assessing the spatial transferability of the instance segmentation model and designing implementation strategies that can improve the spatial transferability of the model.
Figure 1: Dwelling type and structures in three different refugee camps

Suggested/supervised by: Yunya Gao, Stefan Lang

Due to human armed conflicts, human rights violations, persecution, or environmental degradation, numerous people are being forced to flee their homes globally. They are called refugees if they have crossed international borders, or internally displaced persons (IDPs) in the case they are on the run within their home countries. Refugee/IDP dwellings are commonly used as temporary shelters for these displaced people. Detailed information on refugee/IDP dwelling infrastructure, the population in need and their spatial distribution is important for planning humanitarian actions (Sprohnle, Fuchs, & Aravena Pelizari, 2017). However, during a crisis, such critical information is usually hard to access by fieldwork (Witmer, 2015). Therefore, detecting refugee/IDP camps through remote sensing (RS) techniques has attracted a lot of attention.

Very high spatial resolution optical (VHSRO) satellite imagery is considered as the major source of information to identify the number and types of structures (Ghorbanzadeh, Tiede, Wendt, Sudmanns, & Lang, 2021). Visual interpretation of VHSRO imagery is recognized to produce accurate refugee/IDP camp mapping results (Witmer, 2015). However, it is thought to be highly reliant on context knowledge, time-consuming and labour intensive (Witmer, 2015). Recently, deep learning (DL) methods have been proved to outperform many other automatic processing methods in many remote sensing domains. However, DL methods usually require a large amount of data and high computational cost (Zhu et al., 2017). In the past few decades, ZGIS has assisted Doctors without borders (Médecins Sans Frontières, MSF) in collecting a large amount of data of refugee/IDP camps together with VHSRO imagery for many countries. Therefore, DL methods have high potentials in detecting refugee/IDP dwellings. However, by looking through the collected data, there are usually multiple classes of dwellings. The amount of data for each class is imbalanced. After some initial experiments, it is found out that the accuracy of minority classes (e.g. Tukul) is usually much lower than majority classes (e.g. bright dwellings). It is still unknown how to deal with imbalanced data issues in refugee/IDP dwelling detection by using DL methods. Improving the accuracy of prediction results of minority classes may be valuable for planning humanitarian actions.

Figure 1. Three types of refugee/IDP dwellings represented from very high spatial resolution satellite imagery

Reference:

Ghorbanzadeh, O., Tiede, D., Wendt, L., Sudmanns, M., & Lang, S. (2021). Transferable instance segmentation of dwellings in a refugee camp - integrating CNN and OBIA. European Journal of Remote Sensing, 54(sup1), 127–140. https://doi.org/10.1080/22797254.2020.1759456 Sprohnle, K., Fuchs, E. M., & Aravena Pelizari, P. (2017). Object-Based Analysis and Fusion of Optical and SAR Satellite Data for Dwelling Detection in Refugee Camps. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5), 1780–1791. https://doi.org/10.1109/JSTARS.2017.2664982 Witmer, F. D. W. (2015). Remote sensing of violent conflict: eyes from above. International Journal of Remote Sensing, 36(9), 2326–2352. https://doi.org/10.1080/01431161.2015.1035412 Zhu, X. X., Tuia, D., Mou, L., Xia, G. S., Zhang, L., Xu, F., & Fraundorfer, F. (2017). Deep learning in remote sensing: a review. ArXiv, (December). https://doi.org/10.1109/MGRS.2017.2762307

Suggested/supervised by: Lorenz Wendt

Background:In the northern province of Mozambique, Cabo Delgado, Islamic extremist armed groups started aggressive attacks on civilians in October 2017. These attacks have continued in 2021, causing insecurity and displacement within the area, food shortages, and further suffering by the backlash of government forces. An estimated 713,000 people have been internally displaced, mostly from the disputed coastal areas towards more inland regions according to estimates by UNHCR and IOM.

The conflict zone is partly considered a “no access /hard to reach area”, making it difficult to get a comprehensive overview of the situation for humanitarian actors like MSF or UN organisations. The task is therefore to leverage EO data to map the effect of the conflict on people, villages, transport infrastructure and agricultural production. Multitemporal optical imagery from Sentinel-2 and potentially Planetscope shall be used to identify abandoned villages and roads by mapping overgrowth. Newly formed settlements in the safer regions surrounding the conflict zone may be detected by bushland or forest clearing. A reduction of active cropland might indicate displacement of people and/or food insecurity. These analyses can be carried out in Google Earth Engine or other cloud processing platforms, following strategies employed also by the World Food Programme.

In addition, VHR images might be analyzed to map dwellings (tents, huts, etc) of IDPs in selected hotspot locations, for example Pemba City (MOZ). Given the breadth of EO and GI data available, the exact scope, dataset and methodology will be discussed with the MSc candidate individually.

Further reading:

https://www.acaps.org/country/mozambique/crisis/violent-insurgency-in-cabo-delgado https://reliefweb.int/report/mozambique/urgent-needs-mozambique-cabo-delgado-situation-7-may-2021 https://displacement.iom.int/reports/iom-dtm-baseline-assessment-report-round-11-march-2021 https://www.wfp.org/publications/wfp-mali-satellite-imagery

Suggested / supervised by: Stefan Lang, Barbara Hofer, Petra Füreder

Background:
The Christian Doppler Laboratory on Earth Observation for Humanitarian Action – GEOHUM – seeks to advance technologies at the interface of EO*GI and AI (artificial intelligence). The applications range from mission planning and operations in crisis intervention to population estimation for food distribution or vaccination campaigns. Our vision is to enhance technical and organisational capacities matching specific needs from humanitarian organisations, in particular our partner MSF (Doctors Without Borders). Envisaged outcomes are a fundamental scientific substantiation and the development and innovative use of relevant information products to optimize aid delivery in conflict and humanitarian disaster situations.

Over the last ten years, thousands of dwelling representations in temporary refugee settlements were generated through semi-automated image analysis approaches. This happened by and large demand driven, with a very simplified, often ad-hoc defined underlying data model. Different dwelling types were documented over time by differentiating between traditional or regional dwelling types and artificial dwellings (mainly tents, or other makeshift structures). Within the CDL we try to systematize this inventory by generating an ontology and related data model for dwelling types. It contains (1) a time stamp and unique identifier to the underlying image; (2) geometric properties, (size, orientation, shape), c. spectral features (standardized colour code); (3) context-related properties (arrangement.

This master thesis explores existing schemata and dwelling typologies and supports the development of a purpose-driven domain ontology in the context of humanitarian assistance.  it also includes the investigation of a global grid (e.g., based on Sentinel-2 tiling and subdivisions) for space-time models of dwelling dynamics.

Suggested reading:

Lang, S., L. Wendt, D. Tiede, Y. Gao, V. Streifeneder, H. Zafar, A. Adebayo, G. Schwendemann and P. Jeremias (2021). "Multi-feature sample database for enhancing deep learning tasks in operational humanitarian applications." GI Forum - Journal for Geographic Information Science 9(1): 209-219. 

Start:
1 Oct 2021

Prerequisites/qualification:
remote sensing
databases
ontologies