Pytorch implementation of our paper “TransMI: a Transfer-learning method for generalized Map Information evaluation”. We collect a Subjective dataset for deep learning in Information Theory of Cartography named SITC and propose a novel approach, dubbed TransMI, to measure the quality of generalized map information, which is faster, more unified, and more subjective.
- pytorch
- torchvision
- numpy
- pandas
We provide the Subjective dataset for deep learning in Information Theory of Cartography (SITC) in this repository.
File Name / Folder | Content |
---|---|
./data/pre_train/data | Relative SITC dataset |
./data/pre_train/clsLabel.csv | Relative SITC labels |
./data/fine_tune/data | Absolute SITC dataset |
./data/fine_tune/MOS.csv | Absolute SITC MOS |
ReSITC contains 2970 maps, among which there are 20 kinds of relative relationships as shown in the table below.
Label | Class |
---|---|
0 | Increase in Polygon Type |
1 | Increase in Line Type |
2 | Increase in Point Type |
3 | Increase in Point Number |
4 | Increase in Polygon Number |
5 | Increase in Line Number |
6 | Distribution Tends to be Chaotic |
7 | Increase in Point Level |
8 | Increase in Polygon Complexity |
9 | Change in Color |
10 | Change in Point Meaning |
11 | Decrease in Polygon Type |
12 | Decrease in Line Type |
13 | Decrease in Point Type |
14 | Decrease in Point Number |
15 | Decrease in Polygon Number |
16 | Decrease in Line Number |
17 | Distribution Tends to be Orderly |
18 | Decrease in Point Level |
19 | Decrease in Polygon Complexity |
AbSITC contains 330 maps, and each map is scored by 15 participants according to the quality of generalized map information.
You can reproduce the results in the paper according to the following instructions.
To reproduce the k-fold cross-validation results in the pre-training stage:
cd pre_train
python train.py
To generate the pretrained model for the fine-tuning stage:
cd ..
cd generate_ckpoint
python train.py
To reproduce the k-fold cross-validation results in the fine-tuning stage:
cd ..
cd fine_tune
python train.py --model_path ../logs/generate_ckpoint_output/model.pth --model_id 1
Here, we provide some prediction results of our model.