Remote sensing
Remote sensing uses deep learning technology among other methods to understand various aspects of a region including its topological, hydrological, vegetation and other geographic factors.
Satellites built specifically for remote sensing observe and gather data of a region by way of photographs.
These images capture different features of the area like water, soil, vegetation, rocks, etc in detail.
These images are then stitched together to create a map of sorts.
Then, deep learning is employed to build predictive models to determine which regions are vulnerable to a certain ecological disaster.
However, much like other predictive models built by AI like AlphaFold which makes 3D models of protein structures, the outcomes aren’t assured.
The launch of India's first civilian IRS-1A spacecraft in March 1988 marked the start of a successful journey for the Indian Space Programme.
Why remote sensing alone is not enough to predict floods?
Remote sensing provides only a snapshot of environmental conditions over time.
It does not offer real-time predictions or immediate updates on shifting slopes.
Remote sensing cannot capture the continuous changes in slopes or sudden events like cloud bursts effectively
Accurate predictions require ground radar surveys that involve physical monitoring of slopes.
On-ground radar monitoring is expensive and often only used around mining facilities or roads, not always available for all vulnerable areas.
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