Title: Geospatial Data Science for Public Health Surveillance
Talking about the developments of innovative statistical methods and interactive visualization applications for geospatial data analysis and health surveillance

In a world where epidemiology has gone from almost an almost unknown discipline to being a part of our everyday language and lives, due to COVID-19, this talk will explore the importance of the relationship between epidemiology and the geographical perspective

Title: Community-based Tropical Forest Monitoring Using Emerging Technologies

This presentation will describe an integrated community based tropical forest monitoring system, which combines emerging technologies, remote sensing and community-based observation.

Title: Wheat Rust Early Warning System in Ethiopia – Using New Technologies to Combat Crop Disease

During his talk, Gerald will describe the existing early warning system and the planned development of additional, new components for enhancement based on artificial intelligence, machine learning, and remote sensing, such as UAV-based high throughput phenotyping for biotic stresses.

Workshop session A : Disease Risk Modeling and Visualization using R-INLA

In this workshop we will learn how to estimate disease risk and quantify risk factors using aerial and geostatistical data. We will also create interactive maps of disease risk and risk factors, and introduce presentation options such as interactive dashboards. We will work through two disease mapping examples using data of malaria in The Gambia and cancer in Pennsylvania, USA.

Workshop session B : Accessing, mapping, and exporting Nitrogen Dioxide data on Google Earth Engine

In this workshop, we will cover how to access the air pollution data collected by the TROPOMI orbital sensor on Google Earth Engine, with an emphasis on nitrogen dioxide measurement, which has been used in many media examples as a proxy for mapping the intensity of activities during pandemic and lockdown periods.

Workshop session C : Open source based image analysis of multispectral UAV data based on QGIS and OBIA workflows

In this workshop, we will demonstrate the analysis of UAV-based multispectral MICASENSE data (5 band MS camera) along the QGIS and OTB workline. Unsupervised (K-means) and OBIA-based (random forest) supervised classification processes of typical indices (NDVI, SAVI, Soil_RI) will be explained, calculated and evaluated. The exercise data and videos will be provided afterwards to support the self-study-potentials of the participients.