This repository contains codes and data for performing city classification prediction tasks.
Original dataset contains raw 1585 images of 4800 x 4800 resolution (16 GB). Raw data, processed dataset, and models altogether can be downloaded by filling out this form. Alternatively, models only can be accessed at this google drive link.
Dataset consists of 45 cities from various locations, and mostly chosen from Arcadis Index 2022. Cities and their corrresponding index values from 9 different sustainability ranking systems which were used for model training:
| City IATA Code | Overall Arcadis SCI | Planet Arcadis SCI | People Arcadis SCI | Profit Arcadis SCI | Sustainable cities by Corporate Knights | Resilient Cities by Grosvenor | Global Cities by AT Kearney | European Green City Index | US and Canada Green City Index |
|---|---|---|---|---|---|---|---|---|---|
| ALA | - | - | - | - | - | - | 118 | - | - |
| ESB | - | - | - | - | - | - | 86 | - | - |
| ASB | - | - | - | - | - | - | - | - | - |
| NQZ | - | - | - | - | - | - | 128 | - | - |
| GYD | - | - | - | - | - | - | - | - | - |
| BKK | 72 | 92 | 58 | 73 | - | - | 35 | - | - |
| PEK | 73 | 91 | 71 | 53 | 30 | 39 | 6 | - | - |
| FRU | - | - | - | - | - | - | - | - | - |
| BOG | 78 | 20 | 82 | 82 | 36 | - | 63 | - | - |
| BOS | 22 | 54 | 54 | 3 | - | 7 | 21 | - | 6 |
| BNE | 64 | 60 | 57 | 50 | - | 27 | - | - | - |
| AEP | 82 | 62 | 84 | 84 | 35 | 36 | 32 | - | - |
| CAI | 86 | 89 | 79 | 91 | - | 48 | 59 | - | - |
| CHI | 52 | 67 | 70 | 8 | - | 8 | - | 11 | |
| DUB | 37 | 28 | 19 | 65 | - | 29 | 45 | 21 | - |
| HAN | 85 | 93 | 80 | 85 | - | - | - | - | - |
| HKG | 63 | 56 | 65 | 45 | - | 30 | 7 | - | - |
| IST | 74 | 55 | 74 | 79 | 46 | - | 27 | 25 | - |
| CGK | 83 | 68 | 81 | 86 | - | 49 | 67 | - | - |
| FIH | 100 | 99 | 95 | 100 | - | - | 136 | - | - |
| KUL | 71 | 73 | 62 | 69 | - | - | - | - | - |
| LOS | 99 | 88 | 100 | 99 | 40 | - | 113 | - | - |
| LHE | 94 | 95 | 85 | 97 | - | - | 127 | - | - |
| LIS | 57 | 24 | 56 | 66 | - | - | 46 | 18 | - |
| MNL | 93 | 83 | 97 | 89 | - | 47 | 69 | - | - |
| MEL | 60 | 50 | 61 | 43 | - | - | 12 | - | - |
| MEX | 79 | 53 | 83 | 77 | - | 44 | 31 | - | - |
| MIL | 51 | 21 | 39 | 71 | - | 33 | 44 | - | - |
| BOM | 91 | 81 | 89 | 96 | 44 | 46 | 62 | - | - |
| MUC | 19 | 12 | 25 | 27 | - | 24 | 26 | - | - |
| NBO | 96 | 82 | 98 | 95 | - | - | 89 | - | - |
| OSL | 1 | 1 | 17 | 39 | 2 | - | 54 | 3 | - |
| PAR | 8 | 2 | 43 | 31 | 17 | 23 | 3 | 10 | - |
| RIX | 44 | 18 | 30 | 62 | - | - | - | 15 | - |
| SFO | 9 | 35 | 38 | 4 | 16 | 16 | 11 | - | 1 |
| GRU | 84 | 44 | 94 | 78 | 42 | 41 | 40 | - | - |
| ICN | 26 | 43 | 4 | 44 | 25 | 35 | 17 | - | - |
| CIT | - | - | - | - | - | - | - | - | - |
| SIN | 35 | 69 | 5 | 28 | 45 | 32 | 9 | - | - |
| SYD | 33 | 42 | 15 | 46 | 26 | 19 | 15 | - | - |
| TPE | 46 | 71 | 20 | 29 | - | 34 | 49 | - | - |
| TAS | - | - | - | - | - | - | - | - | - |
| TKY | 2 | 7 | 7 | 20 | - | 26 | 4 | - | - |
| YVR | 17 | 26 | 13 | 30 | 8 | 2 | 39 | - | 2 |
| IAD | 20 | 45 | 37 | 15 | 24 | 9 | 14 | - | 8 |
$ git clone https://github.com/IS2AI/city-classification-and-index-prediction
Prior to training it is necessary to perform pre-processing on raw images. To generate patches out of raw images needed for training, and to perform train-val-test split launch the following script:
python3 preprocessing.py
To launch training for city index predition use:
python3 train_regression.py
To test city index prediciton on unseen patches run:
python3 test_regression.py
To create sustainability color map, there is available another script:
python3 make_sustainability_map.py