Nikola Milojevic-Dupont

TL;DR: From smart grids to disaster management, high impact problems where existing gaps can be filled by ML are identified, in collaboration with other fields, to join the global effort against climate change.

TL;DR: It is shown that the emergence of big data and machine learning methods enables climate solution research to overcome generic recommendations and provide policy solutions at urban, street, building and household scale, adapted to specific contexts, but scalable to global mitigation potentials.

TL;DR: In this paper, the authors develop a framework of ten ethical principles and three normative principles for fair street space allocation in urban areas, which is based on the work of the authors of this paper.

TL;DR: This work presents a machine learning based method for predicting building heights, which is based only on open-access geospatial data on urban form, such as building footprints and street networks, and finds that the morphology of the urban fabric surrounding a given building is highly predictive of the height of the building.

TL;DR: In this article , the authors present EUBUCCO v0.1, a database of individual building footprints for ~202 million buildings across the 27 European Union countries and Switzerland, which is relevant for a variety of use cases, e.g., for energy system analysis or natural hazard risk assessments.