Dwelling typology for refugee settlements

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