Rare habitat mapping with deeplearning - NoData class issue?
Hi folks,
I'm trying to detect in Southern Belgium rare and humid grasslands/wetlands. I'm not satisfied with the results and I would like to stress my workflow with the community. Any advise, tips are warmly welcome!
This is kind a large scale project as I want to build an AI model which can track those habitats across the entire region (ca. 17000 km²). I have a reference data set with the delineated patches of the habitats and of course. The reference dataset represents a total amount of 18 km² located across the entire region. The idea is to find those habitats outside these know locations in order to protect them.
As input imagery data, I have two satellite mosaics (superresolution of sentinel 2) covering the area in leaf-off and leaf-on condition (2 * 10 bands), a lidar dtm, dsm and chm and a LULC maps. I resampled (2.5m), normalized (using median and interquartile range) all layers and stacked in one multilayer raster.
For the moment, the best results I got were from a PSP model using two classes, class 1 being the targeted class (rare habitat patches) and 2 the surrounding pixels inside the habitat patch bounding box. I used PSP model after running autoDL which highlighted PSP as the best solution.
Here the results after 49 epochs (just gave you the 3 first):
I think that the NoData (0 in image labels) sort of dilutes the overall accuracy because my targeted habitat patches are quite rare in the landscape, there are a lot of 0 values in the images used to train the model. And I didn't find a way to class balance the nodata (tried ignore_classes = [0] didn't work). I also tried to train a model jst over the habitat patches but didn't improve.
Do you think that my guess about NoData is good? Should I look somewhere else?
Adrien
Thanks for your answer and advises!
First block:
1. 32 bit float. It would be such a pity to reduce lidar data to 8 bit pixel depth
2. data.show_batch: not sure to know what's this object (I'm using GUI). Some screenshots of training sample (here with aerial images).
3. I get this error (only when I ignored the 0 class):
I'm using the GUI
4.
5. I think enough 🙂 I cannot remember but it delivered the result well before reaching the time limit
If you're interested I can share data. I would be more than happy to contribute to a new notebook as I think a lot of people wants to use deep learning with more complex data structure than RGB imagery.
Second batch of advises:
1. Ok but if possible, I would appreciate not to
2. I will surely do!
3. DeepLearning should handle that no? PCA approaches assume linear relationship between predictors and targeted classes. I don't want to check that!
4. Same concern than with pixel depth...
