Detection of hunting pits using airborne laser scanning and deep learning
https://doi.org/10.5878/en98-1b29
This is training and testing data for the detection of hunting pits in airborne laser data. The data is split into three parts. 1: Data for transfer learning with radar imagery and impact craters on the moon. 2. Data for training and testing of the machine learning model. 3: Data from a separate demonstration area used to evaluate the model.
The lunar data (1) were used to pre-train a machine learning model before training on the real data of hunting pits from earth (2). The demonstration data was used to visually evaluate the result of the final model.
All code used to create this dataset and train the machine learning models can be found here: https://github.com/williamlidberg/Detection-of-hunting-pits-using-airborne-laser-scanning-and-deep-learningOpens in a new tab The code is also included in the file "code.zip"
Data files
Data files
Citation and access
Citation and access
Data access level:
Creator/Principal investigator(s):
Research principal:
Principal's reference number:
- SLU.seksko.2023.4.4.IÄ-2
Data contains personal data:
No
Citation:
Language:
Method and outcome
Method and outcome
Unit of analysis:
Population:
Digitized hunting pits
Description of sampling:
Mostly northern Sweden with some pits from southern Sweden.
Time period(s) investigated:
Variables:
11
Number of individuals/objects:
2519
Data format/data structure:
Type of archaeological investigation:
Type of archaeological remains:
Geographic coverage
Geographic coverage
Geographic location:
Geographic description:
Central and northern Sweden
Administrative information
Administrative information
Responsible department/unit:
Forest ecology and management
Other research principals:
Contributor(s):
Funding
Funding
Funding agency:
- Kempe Foundation
Opens a new window at ror.org.
ROROpens in a new tab
Award title:
Framtidens kartor för klimatanpassad skogsskötsel
Funding agency:
- Marcus and Amalia Wallenberg Foundation
Opens a new window at ror.org.
ROROpens in a new tab
Award title:
Wallenberg AI, Autonomous Systems and Software Program – Humanities and Society (WASP-HS)
Funding information:
The project "Challenges and Social Consequences of Artificial Intelligence in Swedish Forests" in the WASP-HS program
Funding agency:
- Marianne and Marcus Wallenberg Foundation
Opens a new window at ror.org.
ROROpens in a new tab
Award title:
Wallenberg AI, Autonomous Systems and Software Program – Humanities and Society (WASP-HS)
Funding information:
The project "Challenges and Social Consequences of Artificial Intelligence in Swedish Forests" in the WASP-HS program
Topic and keywords
Topic and keywords
Standard för svensk indelning av forskningsämnen 2025:
INSPIRE topic categories:
Publications
Publications
Citation:
Lidberg, W., Westphal, F., Brax, C., Sandström, C., & Östlund, L. (2024). Detection of hunting pits using airborne laser scanning and deep learning. In Journal of field archaeology. 49 (6), 395–405. https://doi.org/10.1080/00934690.2024.2364428Opens in a new tab
