Reconstructing historical 3D city models

Historical maps are increasingly used for studying how cities have evolved over time, and their applications are multiple: understanding past outbreaks, urban morphology, economy, etc. However, these maps are usually scans of older paper maps, and they are therefore restricted to two dimensions. We investigate in this paper how historical maps can be ‘augmented’ with the third dimension so that buildings have heights, volumes, and roof shapes. The resulting 3D city models, also known as digital twins, have several benefits in practice since it is known that some spatial analyses are only possible in 3D: visibility studies, wind flow analyses, population estimation, etc. At this moment, reconstructing historical models is (mostly) a manual and very time-consuming operation, and it is plagued by inaccuracies in the 2D maps. In this paper, we present a new methodology to reconstruct 3D buildings from historical maps, we developed it with the aim of automating the process as much as possible, and we discuss the engineering decisions we made when implementing it. Our methodology uses extra datasets for height extraction, reuses the 3D models of buildings that still exist, and infers other buildings with procedural modelling. We have implemented and tested our methodology with real-world historical maps of European cities for different times between 1700 and 2000

Spatial Optimization Methods for Malaria Risk Mapping in Sub-Saharan African Cities Using Demographic and Health Surveys

Vector-borne diseases, such as malaria, are affected by the rapid urban growth and climate change in sub-Saharan Africa (SSA). In this context, intra-urban malaria risk maps act as a key decision-making tool for targeting malaria control interventions, especially in resource-limited settings. The Demographic and Health Surveys (DHS) provide a consistent malaria data source for mapping malaria risk at the national scale, but their use is limited at the intra-urban scale because survey cluster coordinates are randomly displaced for ethical reasons. In this research, we focus on predicting intra-urban malaria risk in SSA cities-Dakar, Dar es Salaam, Kampala and Ouagadougou-and investigate the use of spatial optimization methods to overcome the effect of DHS spatial displacement. We modeled malaria risk using a random forest regressor and remotely sensed covariates depicting the urban climate, the land cover and the land use, and we tested several spatial optimization approaches. The use of spatial optimization mitigated the effects of DHS spatial displacement on predictive performance. However, this comes at a higher computational cost, and the percentage of variance explained in our models remained low (around 30%-40%), which suggests that these methods cannot entirely overcome the limited quality of epidemiological data. Building on our results, we highlight potential adaptations to the DHS sampling strategy that would make them more reliable for predicting malaria risk at the intra-urban scale

The Multi-Satellite Environmental and Socioeconomic Predictors of Vector-Borne Diseases in African Cities:Malaria as an Example

Remote sensing has been used for decades to produce vector-borne disease risk maps aiming at better targeting control interventions. However, the coarse and climatic-driven nature of these maps largely hampered their use in the fight against malaria in highly heterogeneous African cities. Remote sensing now offers a large panel of data with the potential to greatly improve and refine malaria risk maps at the intra-urban scale. This research aims at testing the ability of different geospatial datasets exclusively derived from satellite sensors to predict malaria risk in two sub-Saharan African cities: Kampala (Uganda) and Dar es Salaam (Tanzania). Using random forest models, we predicted intra-urban malaria risk based on environmental and socioeconomic predictors using climatic, land cover and land use variables among others. The combination of these factors derived from different remote sensors showed the highest predictive power, particularly models including climatic, land cover and land use predictors. However, the predictive power remained quite low, which is suspected to be due to urban malaria complexity and malaria data limitations. While huge improvements have been made over the last decades in terms of remote sensing data acquisition and processing, the quantity and quality of epidemiological data are not yet sufficient to take full advantage of these improvements

Automatic reconstruction of 3D city models from historical maps

Historical 3D city models have been increasingly used for the preservation and communication of the cultural heritage to a wider and more diversified public. In the recent years, they have also been of a growing interest in other domains such as in urbanism or in economy. However, their potential for supporting new use cases has been restricted by the difficulty to generate these models. Historical 3D city models can only be reconstructed from historical sources, such as historical maps, and this means dealing with all sorts of constraints and inaccuracies. As a result, reconstructing historical 3D city models is a challenging process that is to date still essentially manual and time-consuming. This thesis investigates to what extent the reconstruction of historical 3D city models can be automated. Several existing methods for extracting building footprints from historical maps have been tested and compared so as to identify the pros, cons and use cases of each method and all the challenges of working with historical maps. Based on these experiments a fully automated methodology was developed. It relies on three main stages: (1) the processing of the historical maps to extract the building plots, (2) the subdivision of these building plots into individual building footprints and (3) the reconstruction of a LoD2 historical 3D city model using 3D procedural modelling. This methodology was implemented with historical maps from two different study areas, Delft and Brussels, and for different epochs in order to reconstruct a dynamic historical 3D city model for these cities. The results show that the methodology workflow developed in this thesis allows to reconstruct automatically historical 3D city models for different historical maps collections and for different study areas. The main differences between the two case studies, Delft and Brussels, regard the implementation details (i.e. data availability, running time and user-defined parameters) but similar results are obtained, which show the suitability of the methodology to be applied for other study areas. Two elements are identified as main factors influencing the quality of the results obtained: the quality of the scanning process and the symbology of the historical maps. For historical maps that were properly scanned, with sufficient spatial resolution and strict symbology rules, the methodology provides accurate results by identifying more than 84% of the building plots in the ground truth and classifying properly more than 89% of the building plots. In addition, all historical 3D city models reconstructed have their geometries valid at more than 99%. Overall, this thesis provides a methodology for reconstructing automatically historical 3D city models from historical maps along with guidance and hints about this process and about a series of other methods, so that any user can find the most suitable method for their needs. All source codes and data of this thesis are available at https://github.com/camilleMorlighem/histo3d. Geomatic

Deployment of Indoor Point Clouds for Firefighting Strategy

The Deployment of Indoor Point Clouds for Firefighting Strategy project was realised as a Synthesis Project of the Geomatics Master Programme of the Built Environment Faculty at the Technical University of Delft. This project was executed by a team of five Master students in collaboration with the Dutch response team collective Veiligheidsregio Rotterdam-Rijnmond. The objective of this project is to develop an information system that makes use of indoor data to support tactical decision-making during fire emergency responses. The main challenge that response teams are facing when they develop deployment plans is the lack of appropriate information about indoor spaces. As a result, response teams may end up relying on inaccurate assumptions which can lead to dangerous situations. New technologies such as SLAM devices and augmented reality displays, combined with processing techniques, can be used to supply them with the information needed to make the right choices. The result of this project is a prototypical information system containing an interactive, 3D environment that can receive updates, merge data from different data sources, and accommodate mixed reality information sharing in real-time.Synthesis Project 2020Geomatic

Spatial Optimization Methods for Malaria Risk Mapping in Sub-Saharan African Cities Using Demographic and Health Surveys

Vector-borne diseases, such as malaria, are affected by the rapid urban growth and climate change in sub-Saharan Africa (SSA). In this context, intra-urban malaria risk maps act as a key decision-making tool for targeting malaria control interventions, especially in resource-limited settings. The Demographic and Health Surveys (DHS) provide a consistent malaria data source for mapping malaria risk at the national scale, but their use is limited at the intra-urban scale because survey cluster coordinates are randomly displaced for ethical reasons. In this research, we focus on predicting intra-urban malaria risk in SSA cities-Dakar, Dar es Salaam, Kampala and Ouagadougou-and investigate the use of spatial optimization methods to overcome the effect of DHS spatial displacement. We modeled malaria risk using a random forest regressor and remotely sensed covariates depicting the urban climate, the land cover and the land use, and we tested several spatial optimization approaches. The use of spatial optimization mitigated the effects of DHS spatial displacement on predictive performance. However, this comes at a higher computational cost, and the percentage of variance explained in our models remained low (around 30%-40%), which suggests that these methods cannot entirely overcome the limited quality of epidemiological data. Building on our results, we highlight potential adaptations to the DHS sampling strategy that would make them more reliable for predicting malaria risk at the intra-urban scale. Global climate change and rapid urbanization in sub-Saharan Africa (SSA) are likely to affect the epidemiology of vector-borne diseases such as malaria in urban and peri-urban areas. In this context, a better understanding of intra-urban malaria risk and its determinants has become even more urgent. Malaria risk has often been modeled at the national scale from Demographic and Health Surveys (DHS), which are periodically conducted in more than 90 developing countries. However, survey cluster coordinates in DHS are randomly displaced by up to 2 km in urban areas to protect respondent privacy, which reduces the accuracy of malaria models and risk maps at the intra-urban scale. In this study, we tested the potential of spatial optimization methods to overcome the effect of DHS displacement. We found that spatial optimization methods improved the performance of malaria models, but the improvement in performance is small for a higher computational cost. With these methods, we predicted malaria risk in several SSA cities (Dakar, Dar es Salaam, Kampala and Ouagadougou). We expect the quality and quantity of available data on malaria and other vector-borne diseases to improve in the future, which will certainly make these methods extremely useful in the fight against these diseases. We tested spatial optimization approaches to overcome the effect of cluster spatial displacement in Demographic and Health Surveys (DHS)Spatial optimization reduced the effect of displacement, but the percentage of variance explained in malaria models remained lowWe proposed potential adaptations to the DHS sampling strategy to better support the study of malaria risk at the intra-urban scale