Mapping New Informal Settlements using Machine Learning and Time Series Satellite Images: An Application in the Venezuelan Migration Crisis

Since 2014, nearly 2 million Venezuelans have fled to Colombia to escape an economically devastated country during what is one of the largest humanitarian crises in modern history. Non-government organizations and local government units are faced with the challenge of identifying, assessing, and monitoring rapidly growing migrant communities in order to provide urgent humanitarian aid. However, with many of these displaced populations living in informal settlements areas across the country, locating migrant settlements across large territories can be a major challenge. To address this problem, we propose a novel approach for rapidly and cost-effectively locating new and emerging informal settlements using machine learning and publicly accessible Sentinel-2 time-series satellite imagery. We demonstrate the effectiveness of the approach in identifying potential Venezuelan migrant settlements in Colombia that have emerged between 2015 to 2020. Finally, we emphasize the importance of post-classification verification and present a two-step validation approach consisting of (1) remote validation using Google Earth and (2) on-the-ground validation through the Premise App, a mobile crowdsourcing platform

Multidisciplinary assessment of a year 2035 turbofan propulsion system

A conceptual design of a year 2035 turbofan is developed and integrated onto a year 2035 aircraft model. The mission performance is evaluated for CO2, noise and NOx and is compared with a notional XWB/A350-model. An OGV heat exchanger is then studied rejecting heat from an electric generator, and its top-level performance is evaluated. The fan, the booster and the low-pressure turbine of the propulsion system are subject to more detailed aero design based on using commercial design tools and CFD-optimization. Booster aerodynamic modelling output is introduced back into the performance model to study the integrated performance of the component. The top-level performance aircraft improvements are compared to top-level-trends and ICAO estimates of technology progress potential, attempting to evaluate whether there is some additional margin for efficiency improvement beyond the ICAO technology predictions for the same time frame

Measuring OpenStreetMap building footprint completeness using human settlement layers

Orden, A., Flores, R.A., Faustino, P., & Samson, M.S. (2020). Measuring OpenStreetMap building footprint completeness using human settlement layers In: Minghini, M., Coetzee, S., Juhász, L., Yeboah, G., Mooney, P., Grinberger, A.Y. (Eds.). Proceedings of the Academic Track at the State of the Map 2020 Online Conference, July 4-5 2020. Available at https://zenodo.org/communities/sotm-202

Science of the Total Environment / Success factors for citizen science projects in water quality monitoring

Attempts to monitor the quality of freshwater resources on a global scale unveil huge data lacks. Involving citizens in data collection has potential to resolve this lack of water quality data. However, it is widely unclear which factors drive the success of citizen science activities. Based on a systematic literature review of 56 peer-reviewed research articles, we identify three sets of factors for successful citizen science projects in water quality monitoring: (i) attributes of citizens (knowledge and experience in collecting data, awareness of environmental problems, motivation, and socio-economic background of citizens), (ii) attributes of institutions (motivation, type of organization, consistent and adequate funding), and (iii) the interactions between citizens and institutions (supporting structure, communication and feedback). These three sets of factors enable a systematic analysis and design of citizen science projects in the future