@rapiduplift @JamwalNidhi SRTM does have vertical bias however the correction methodology is not sound enough to overcome that in a more accurate manner.
The problem I see is with ML algorithm which uses LIDAR data and ~23 parameters from developed countries as training and testing datasets
The problem I see is with ML algorithm which uses LIDAR data and ~23 parameters from developed countries as training and testing datasets
@rapiduplift @JamwalNidhi These parameters come in coarse resolution datasets (some of them as coarse as a km). Also, The relationship between those parameters and Elevation bias would vary between developed and developing countries. However the training data doesn't account this.
@rapiduplift @JamwalNidhi For eg, population density is related to Building heights to some extent. But it cannot be assumed as the same for US and India. So it is likely to exaggerate bias in India
Params like Vegetation r also problematic as they report difficulty in estimating for places like Florida
Params like Vegetation r also problematic as they report difficulty in estimating for places like Florida
@rapiduplift @JamwalNidhi Personally not a fan of bath tub analytics of climate related sea level change as they don't take into account of the ocean dynamics. It's good for short term (like storm surge) estimates. But long term not very goos. Sedimentation n other processes are not even taken to account
@rapiduplift @JamwalNidhi Sorry. Having difficulty typing my full commentary here.
I have attempted sea level rise algorithms in many Indian states, my opinion is that if we can't take ground data, then there's no one method/ dataset that can answer these problems for all places at the same time.
I have attempted sea level rise algorithms in many Indian states, my opinion is that if we can't take ground data, then there's no one method/ dataset that can answer these problems for all places at the same time.
