NEUROPUBLIC participates in the 4th International Electronic Conference on Remote Sensing

The 4th International Electronic Conference on Remote Sensing, part of the International Electronic Conference on Remote Sensing series, took place online between 25–27 of January 2022. Focusing on “Advances in Remote Sensing for the 17 Sustainable Development Goals”, the Conference aimed to

• promote the use of remote sensing and geospatial information technology,
• address challenging problems,
• share the latest scientific progress,
• minimize limitations and issues,
• exchange innovative approaches, to demonstrate evidence-decision making processes,
• enhance forward thinking for remote sensing,
• formulate better management plans using geospatial data,
• promote the value of remote sensing, and
• draw a better plan for the future of geospatial information technology.

NEUROPUBLIC’s team, consisting of Yorgos Efstathiou, Anastasia Dagla, Michail Tziotis & Nikolaos Marianos, authored and submitted an abstract describing an innovative technological solution that addresses the issue of costly, time consuming, labor intensive but most importantly error prone crop damage assessment that takes place after extreme events.

Mr. Yorgos Efstathiou was one of the invited speakers of the Conference, and presented NEUROPUBLIC’s automated method which focuses on estimating crop damage remotely, at parcel level and near-real time. By integrating satellite imagery and IoT-acquired field data from NEUROPUBLIC’s telemetric agrometeorological stations, the solution allows to:

(i) detect extreme events (frost, hail, flood, drought, windstorm etc.) at local scale, and

ii) visualize the influenced areas by creating damage maps that indicate the area, severity and persistence of damaged crops.

It is important to note that the results of the proposed solution have been validated by agro-insurance appraisers, and that the next steps include the further development of this service by performing a correlation analysis between crop damage and yield loss and by generating time-series that could add valuable information regarding climate change impact on yield loss.