The Urban Analytics Lab seminar series is part of our community engagement and to provide a platform leading to interesting discussions, new ideas, and spurring collaborations. In our seminar series, we give priority to early career researchers. The series is managed by Dr Marcel Ignatius.
As an emerging topic, OpenStreetMap (OSM) has been greatly improved via recent humanitarian mapping campaigns, and intensively used to support humanitarian aid activities, especially in sub-Saharan Africa. Considering its time-crucial nature, how to create timely and accurate maps of OSM missing features (e.g., buildings and roads) become a vital challenge. In our project DeepVGI, we study predictive analytics methods with remote sensing images, VGI, and social media data (e.g., Twitter) via advanced deep learning algorithms. The experiments results, especially in sub-Saharan Africa countries, demonstrate the capability of GeoAI in improving the OSM data completeness, and more importantly show great potential in supporting better and faster humanitarian mapping from a machine-assisted mapping perspective.
In many cities around the world. Nature’s Contribution to People (NCP) are under pressure. With a growing urban population, the pressure on NCPs in the remaining open spaces increases. The planning of new residential areas to accommodate urban dwellers increasingly requires the consideration of NCPs, especially when taking into account the irreversibility of urban development. In a compact and land-scarce city like Singapore, the allocation of new residential areas poses a major challenge of high complexity. Designed to operationalize and solve such complex problems, optimization procedures can identify trade-offs between multiple objectives by displaying the optimal solutions in a so-called trade-off curve. A solution is considered optimal if an objective cannot be improved, without reducing another objectve. Furthermore, multi-objective optimizations allow the integration of various perspectives of stakeholders into the modeling process, making them well-suited to be integrated in participatory approaches.
The study of urban morphology is a well established field for understanding the impact and the management of the urban form and the built environment which is useful in many different applications. While there have been a plethora of studies in 2D, there have been very little in 3D, of which a vast majority are not in fact true 3D and rather 2.5D. Our work aims to project urban morphology analysis to true 3D. This work is driven in large part due to the availability of open 3D city models as well as new 3D Python libraries. In our presentation we will examine 3D urban morphology metrics, the process of moving from 2D to 3D, difficulties in working with 3D data, and applications that can benefit from this analvsis.