Folder Structure

Prepare the following folders to organize this repo:

├── MAMLN (code)
├── pretrain_weights (save the pretrained weights like vit, swin, etc)
├── model_weights (save the model weights)
├── fig_results (save the masks predicted by models)
├── lightning_logs (CSV format training logs)
├── data
│   ├── LoveDA
│   ├── uavid
│   ├── vaihingen
│   ├── potsdam 

Install

Open the folder airs using Linux Terminal and create python environment:

conda create -n airs python=3.8
conda activate airs

conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
pip install -r GeoSeg/requirements.txt

Pretrained Weights

Quark Netdisk : rEsN

Data Preprocessing

Quark Netdisk : YxVA

Training

python MAMLN/train_supervision.py -c MAMLN/config/uavid/***.py

Use different config to train different models.

Validation

For example:

python MAMLN/loveda_test.py -c MAMLN/config/loveda/***.py -o fig_results/loveda/*** --rgb --val -t 'd4'

Testing

LoveDA

python MAMLN/loveda_test.py -c MAMLN/config/loveda/***.py -o fig_results/loveda/*** -t 'd4'

UAVid

python MAMLN/inference_uavid.py \
-i 'data/uavid/uavid_test' \
-c MAMLN/config/uavid/***.py \
-o fig_results/uavid/*** \
-t 'lr' -ph 1152 -pw 1024 -b 2 -d "uavid"

Inference on huge remote sensing image

python MAMLN/inference_huge_image.py \
-i data/vaihingen/test_images \
-c GeoSeg/config/vaihingen/***.py \
-o fig_results/vaihingen/*** \
-t 'lr' -ph 512 -pw 512 -b 2 -d "pv"

Reproduction Results

Method	Dataset	F1	OA	mIoU	model_weight
MAMLN	Vaihingen	91.18	91.63	84.02	Quark Netdisk : 1f29
MAMLN	Potsdam	93.37	91.95	87.77	Quark Netdisk : 3DTA
MAMLN	LoveDA	-	-	53.11	Quark Netdisk : RC25
MAMLN	UAVid	-	-	70.51	Quark Netdisk : ejQa

Due to some random operations in the training stage, reproduced results (run once) are slightly different from the reported in paper.

Acknowledgement

Thanks to the GeoSeg framework for the help of the above code implementation of this work, and to the hardware support provided by the Advanced Computing Center of China Three Gorges University.