Automated building construction year detection using historical Swiss maps
This project is a python translation of https://github.com/swiss-territorial-data-lab/regbl-poc by the Swiss Territorial Data Lab. We changed some functionalities, especially to make the code faster and more modular, and detect smaller buildings.
- Install the latest version of Python
- Install the dependencies listed in
requirements.txt, you can use the commandpip install -r requirements.txt
- The map should be divided into tiles. Each tile must be in a .tif format and come with its attached .twf having the same name.
- The map tiles must have their name ending with the tile ID, the year and a final number separated by underscores, in following the format :
tile_name_ID_year_number.tif. For example :SMR25_LV95_KREL_1123_2020_1.tiffor a tile of ID 1123 in year 2020.
The input file lists all the building to detect. It is a CSV file with at least the following columns (other columns are ignored) :
- EGID : EGID of the building
- GKODE : Easting coordinate of the building
- GKODN : Northing coordinate of the building
Complete the configuration file parameters.py following your needs.
Here are the main parameters and their meaning:
INPUT_FOLDERS: List of folders in which the map tiles (.tif files) and their georeferencing (.tfw) are located.TILE_TO_PROCESS: List of tiles to process. Process and generate the output of all these tiles one by one.DATA_LOCATION: Path to the input file (CSV file containing the columns EGID, GKODE, GKODN)GROUND_TRUTH: CSV file similar to the file in DATA_LOCATION, but with the added column "GBAUJ" that indicates the building construction year.OUTPUT_FOLDER: folder where the output CSV file and the tracking images will be generated. The script will create one folder per tiles to process in this folder.
The run the program, run the main.py script, with the command python main.py or python3 main.py
The script go through 7 steps for each tile :
- Initialization : Parse the .tif and .tfw files, resize them and copy them to the processing folder
- Segmentation : Convert full-color maps to black and white images, keeping only the buildings and other large black structures
- Bootstrapping : Extract information from the input CSV file and store them in the processing folder structure
- Detection : Infer for each building
- If it is present or not on each map
- The position, if found
- The area if found
- If the shape changed significantly from the previous year
- Deduction : Read the output from the previous step and outputs a construction period. Also detects if the building significantly changed shape or if the detection oscillates between years. Outputs in the output folder a CSV file with all the results.
- Test : If a ground truth is provided, compare the results with it
- Track : Generates, in the output folder, for all buildings, an image that displays map extracts with detection results. Useful for manual errors inspections.
The script creates in OUTPUT_FOLDER one folder per tile. A tile's folder contains a file result.csv with the following columns :
- EGID : ID of the building
- LAST_NOT_DETECTED : Last year when the building was not found.
- FIRST_DETECTED : First year when the building was present on the map
- NOTE : Additional information about the detection. Currently, there are two notes :
- OSCILLATE : The detection oscillated between found and not found. This often indicates that the building was not present on all the maps, or that the detection failed.
- SHAPE_CHANGED : The ground area detected for the building significantly changed after the building was constructed. This often indicates that a building was modified / rebuild or that the detection failed.
From our test, we recommend discarding the buildings with a OSCILLATE or SHAPE_CHANGED note, as these are generally errors or edge cases where a construction period is ambiguous.
The script also generates one image per building that displays the detection results in the following format :
