README.md

metadata

license: apache-2.0
language:
  - en
tags:
  - Pytorch
  - mmsegmentation
  - segmentation
  - Crop Classification
  - Multi Temporal
  - Geospatial
  - Foundation model
datasets:
  - ibm-nasa-geospatial/multi-temporal-crop-classification
metrics:
  - accuracy
  - IoU

Model and Inputs

The pretrained Prithvi-100m parameter model is finetuned to classify crop and other land cover types based off HLS data and CDL labels from the multi_temporal_crop_classification dataset.

This dataset includes input chips of 224x224x18, where 224 is the height and width and 18 is combined with 6 bands of 3 time-steps. The bands are:

1. Blue
2. Green
3. Red
4. Narrow NIR
5. SWIR 1
6. SWIR 2

Labels are from CDL(Crop Data Layer) and classified into 13 classes.

The Prithvi-100m model was initially pretrained using a sequence length of 3 timesteps. For this task, we leverage the capacity for multi-temporal data input, which has been integrated from the foundational pretrained model. This adaptation allows us to achieve more generalized finetuning outcomes.

Code

Code for Finetuning is available through github

Configuration used for finetuning is available through config.

Results

The experiment by running the mmseg stack for 80 epochs using the above config led to the following result:

Classes	IoU	Acc
Natural Vegetation	0.4038	46.89%
Forest	0.4747	66.38%
Corn	0.5491	65.47%
Soybeans	0.5297	67.46%
Wetlands	0.402	58.91%
Developed/Barren	0.3611	56.49%
Open Water	0.6804	90.37%
Winter Wheat	0.4967	67.16%
Alfalfa	0.3084	66.75%
Fallow/Idle Cropland	0.3493	59.23%
Cotton	0.3237	66.94%
Sorghum	0.3283	73.56%
Other	0.3427	47.12%

aAcc	mIoU	mAcc
60.64%	0.4269	64.06%

It is important to acknowledge that the CDL (Crop Data Layer) labels employed in this process are known to contain noise and are not entirely precise, thereby influencing the model's performance. Fine-tuning the model with more accurate labels is expected to further enhance its overall effectiveness, leading to improved results.

Inference

The github repo includes an inference script an inference script that allows to run the hls-cdl crop classification model for inference on HLS images. These input have to be geotiff format, including 18 bands for 3 time-step, and each time-step includes the channels described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in order. There is also a demo that leverages the same code here.