Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Characterization of urban land use and urban land dynamics in data scarce regions
The aim of this thesis is to advance the characterization of urban land and urban land dynamics to further the understanding of urban areas in data scarce regions. In order to achieve this, this thesis will answer the following research questions: RQ1: How can the integration of satellite imagery and socioeconomic data contribute to mapping urban land use (at a large spatial scale)? RQ2: How can we further the understanding of urban structure in cities located in data scarce regions? RQ3: What insights do building-level changes provide in urban dynamics? Chapter 2 uses a combination of open-source satellite imagery and socioeconomic data to classify urban land use at a national scale, using a deep learning approach. Combining Sentinel-2 and Sentinel-1 imagery with statistics from POIs and road networks increases the overall accuracy of the classification for the Netherlands by 3 percentage points. The Netherlands was divided into four regions, to test whether the combination of data types increased the transferability of the approach. When trained on three regions and tested on the independent fourth one the results showed a clear increase in classification accuracy between 3 and 5 percentage points relative to only using satellite data. However, when trained on one region and testing on another the results varied more strongly with differenced between 0 and 9 percentage points. Chapter 3 produces urban land use maps of three East African cities from satellite imagery and building footprint data. This chapters shows that the required amount of reference data needed when classifying new cities, using a combination of data sources, can be reduced by an order of magnitude by using a transfer learning approach. The combination of freely available PlanetScope satellite data, and publicly available Google building footprint data means that this approach is more cost effective compared to using VHR imagery. Despite using lower resolution imagery, the achieved classification accuracy was comparable to studies using VHR ...
Characterization of urban land use and urban land dynamics in data scarce regions
The aim of this thesis is to advance the characterization of urban land and urban land dynamics to further the understanding of urban areas in data scarce regions. In order to achieve this, this thesis will answer the following research questions: RQ1: How can the integration of satellite imagery and socioeconomic data contribute to mapping urban land use (at a large spatial scale)? RQ2: How can we further the understanding of urban structure in cities located in data scarce regions? RQ3: What insights do building-level changes provide in urban dynamics? Chapter 2 uses a combination of open-source satellite imagery and socioeconomic data to classify urban land use at a national scale, using a deep learning approach. Combining Sentinel-2 and Sentinel-1 imagery with statistics from POIs and road networks increases the overall accuracy of the classification for the Netherlands by 3 percentage points. The Netherlands was divided into four regions, to test whether the combination of data types increased the transferability of the approach. When trained on three regions and tested on the independent fourth one the results showed a clear increase in classification accuracy between 3 and 5 percentage points relative to only using satellite data. However, when trained on one region and testing on another the results varied more strongly with differenced between 0 and 9 percentage points. Chapter 3 produces urban land use maps of three East African cities from satellite imagery and building footprint data. This chapters shows that the required amount of reference data needed when classifying new cities, using a combination of data sources, can be reduced by an order of magnitude by using a transfer learning approach. The combination of freely available PlanetScope satellite data, and publicly available Google building footprint data means that this approach is more cost effective compared to using VHR imagery. Despite using lower resolution imagery, the achieved classification accuracy was comparable to studies using VHR ...
Characterization of urban land use and urban land dynamics in data scarce regions
Rosier, Job Fabian (Autor:in)
22.04.2025
Rosier , J F 2025 , ' Characterization of urban land use and urban land dynamics in data scarce regions ' , PhD , Vrije Universiteit Amsterdam . https://doi.org/10.5463/thesis.1049
Buch
Elektronische Ressource
Englisch
Mapping ecosystem service flows with land cover scoring maps for data-scarce regions
| BASE | 2015