Recent weeks have seen many episodes of heavy to extremely heavy rainfall across many parts of India, especially hilly regions. While rains gave a (much needed) respite from the scorching heat of the summer months (especially in North India), adverse events such as cloud burst, flooding and landslides during this period caused great harm to man & material, alike. At such times, authorities face a daunting task to not only respond quickly to manage the damage caused by these weather phenomena but also ensure rehabilitation of lives and livelihoods post such adverse events. Authorities can leverage granular & dynamic data intelligence systems to manage damage & post-damage scenarios more effectively.

Last year’s monsoon season, while helping a government administrative body, in the aftermath of floods in northeast India, we developed a geospatial data intelligence platform to rapidly identify areas affected by floods, the severity of water levels and their variation across regions, period of inundation, crop patterns at land parcel level and ultimately the magnitude of destruction of croplands in these areas using Geospatial analytics. Using this kind of readily available data intelligence, administrative bodies can enable timely & fair damage claims redressal and even insurers can underwrite credit risk with high degree of precision.

Through our data intelligence system we observed significant variation across land parcels and villages both in terms of flood metrics but also cropping patterns. Some regions in northeast India had suffered severe flooding in the months of May to June of 2020. Our satellite images presented above represent the extent of flood from 20th May to 10th June, 2020. Our platform scanned a large landscape and instantly measured that nearly 30% land area (under monitoring) is inundated with high water levels with varying level of depth across the landscape. However, the inundated area reduced from 30% of the total monitored area between May 20 – May 30 to roughly 11% from 31st May to 10th June.