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TOOLS FOR MAPPING HUMAN EXPOSURE TO RISKY ENVIRONMENTAL CONDITIONS BY MEANS OF GROUND AND EARTH OBSERVATION DATA

Periodic Reporting for period 2 - EOXPOSURE (TOOLS FOR MAPPING HUMAN EXPOSURE TO RISKY ENVIRONMENTAL CONDITIONS BY MEANS OF GROUND AND EARTH OBSERVATION DATA)

Período documentado: 2019-03-01 hasta 2022-08-31

The goal of the EOXPOSURE project is to build methods to quantify the exposure of population and economic assets to multiple risks using novel information provisions from current and future Earth Observation (EO) missions, as well as from the growing sensor web on the ground.

The cutting-edge fusion of this concept with EO and sensor data aims at measuring human exposure to threats that are external to each individual, and quantify the interactions between human beings and the environment. By building geospatial information tools upon data coming from multiple sources, at different spatial and temporal scales, the EOxposure project aims at providing free public services, enabling:

- citizens to understand the threats, and

- decision makers to mitigate risks.

Specifically, EOxposure focuses on threats connected to housing conditions, disease spread, as well as security and health issues in urban and peri-urban areas, where population is concentrated. The new tools build upon the consortium expertise on nutrition- and vector-borne disease models, urban heat monitoring and material characterisation, satellite data processing, and geospatial data fusion, realising interdisciplinary working groups dedicated to the above mentioned applications. EOxposure enrolls institutions from Europe:

- Univ. of Pavia, Italy (coordinating institution)
- Univ. of Extremadura, Caceres, Spain
- Royal Military Academy of Belgium

and South America:

- National Univ. of Cordoba, Argentina
- Federal Univ. of Alagoas, Brazil

merging expertise on exposure to risk in both developed and developing countries.
Tools for Housing Condition Mapping (HOCOM) were developed, which are useful for identifying buildings footprints, height, regularity, and density from VHR satellite images.

Further HOCOM contributions include the use of multi-temporal change detection by taking advantage of exchanged expertise from RMA during secondments. Specifically, we are using state-of-the-art techniques on multi-temporal change detection to monitor subsoil movement by using PS-InSAR - Persistent Scattering Interferometry SAR. This on-going work is part of Master joint-advisorship between University of Brussels, RMA and UFAL. This contribution is planned to be applied to Brazilian relevant problems such as monitoring landslides, dams, and the current problem of the Pinheiro neighborhood in Maceió city.

Regarding disease spread proxies, areas most exposed to risks of viruses carried by the Aedes Aegypti mosquito specie include Latin America, Central Africa and South-East Asia, with major disease spread outbreaks already recorded in Brazil, Argentina Colombia and Venezuela. The disease incidences recorded have been shown to correlate with mosquito vector population density. Major environmental conditions that have already proven to influence the life cycle and population density of mosquitoes include air temperature, precipitation, moisture, and vegetation condition – all reasonably measurable from freely available EO data products.
We have developed a procedure for modelling Aedes Aegypti mosquito specie based on time-series environmental variables obtainable from NASA’s MODIS EO data products and geolocated mosquito population data. To advance the state-of-the-art in this domain, our method uses non-linear relationship measures to extract the most informative features from a larger set of data representing hypothesized environmental conditions that affect the life and abundance of mosquito vectors. By this, our procedure reduces the computational redundancy and model complexity diseases spread models, and thus is application to big data. Furthermore, our procedure creates a process for empirical explainability of the local conditions that mostly affect the temporal variance of Aedes Aegypti vectors, thereby supporting principal investigators in the field of epidemiology in making informed decisions.
We are currently applying our procedure to the city of Vila Velha in Brazil and working with Universidade Federal de Alagoas (UFAL) to extend the functionality of the core algorithms implemented within our processing chain.

Regarding mapping physical proxies to security threats, PHYMAP tools aim at providing an easy way to map and monitor materials that relate to security threats against public health or safety. These proxies include landcover (roads and water bodies) landuse (settlements, crop fields and forests) and population density. Generating these proxies with high quality requires the integration of data from multisource or multitemporal Earth observation data at various spatial resolutions and quality together with open sourced data and contextual information.
PHYMAP toolbox facilitates the mapping of proxies by providing and integrating processing scheme and algorithms into a user friendly interface.

Considering the huge data sets and the required computing resources, we created PHYMAP in a cloud computing environment. The platform consists of components that support both real-time and batch processing and is ingested using SQL for optimization and distribution.
The HOCOM tools address segmentation and classification of neighborhood blocks using med-res satellite data, and single houses using very-high-res imagery. The tools also allow monitoring changes in housing conditions using multi-temp data. Results from a HOCOM study on the Argentinian city of Cordoba suggested that those people who live in urban areas can suffer more heat waves impact than the people who live in the surroundings, regardless of land cover class.

A disease spread proxies (DISESP) toolset has been issued, a multisource data fusion and interpretation toolset able to use disease spread proxies (vegetation status, availability of infrastructures and cultivations, health and medicine records) in a way that could suit different areas and requirements of users, from decision makers to citizens. The automated egg counter can determine very quickly the number of Ae. aegypti eggs present in ovitraps and thus generate timely information to assist government agencies and vector control programs in decision making.

DISESP covers the complete system life-cycle including a setup of the technical solution, requirements, defined architecture and tools used, some details about the theoretical base of the implemented algorithms and procedures, to finally to show results and discussions about the next action items.

A design of a common data and processing gateway has been issued, which enables the integration of HOCOM, DISESP, and PHYMAP tools.

A procedure was also developed for mapping vegetation in urban areas at high resolution using Sentinel-2 data. Condition and content of green spaces are good proxies to quality of life and living conditions in urban areas. Thus, the result of this procedure will serve as input to housing condition mapping, disease spread proxies and physical proxies mapping procedures.

PHYMAP was developed using State of the art Machine Learning open-source algorithms and it has proven it can map and monitor materials that may be proxy to security threats against public health or safety. Multisource and multitemporal EO data at various spatial resolutions and quality were integrated. The algae bloom monitoring tool can be used for operational water quality monitoring of San Roque reservoir, the main potable water source for Córdoba city which hosts 1.8 million habitants.
Urban vegetation processing chain
Logo of the Eoxposure Project
PHYMAP cloud computing architecture