Work package 1, status: completed.
The first WP focused mainly on collecting very high-resolution imagery of dry unvegetated rocky surfaces in California with a drone to provide us with (1) data to train the automatic boulder detection algorithm, (2) ground truth boulder data and (3) investigate the influence of the environment and illumination condition during image acquisition on the detected number of boulders. Two locations with significant boulder density were selected in the Sierra Nevada (California). The fieldwork campaign was conducted at the Obsidian and South Deadman volcanic domes in June 2022, and at the Courtright reservoir in September 2022. At each location, drone images were acquired multiple times during the day (under different illumination conditions), and we manually measured between 20 and 30 boulders. This provided us with very high-resolution boulder data and ground truth data. The latter was significant to investigate the model's performances in the first manuscript.
Work package 2, status: completed.
A very long project period was used to collect additional boulder data from satellite imagery of the surface of the Moon and Mars. This process is very time-consuming (this is why we want to automatize this process!) as there are many boulders. Unfortunately, we had to go through this process as deep learning algorithms require many images to learn detection patterns automatically. A big chunk of 2022 and early 2023 was therefore used in the digitization of boulders, familiarization with the model architecture, conduction of pre-processing steps, coding of scripts to manipulate vector and raster data, and training and fine-tuning of the deep learning model. The current version of our neural network, BoulderNet, was trained from a data set of >33,000 boulders in >750 image tiles from Earth, the Moon, and Mars. During early and mid-2023, the time was used to write the first manuscript and continuously improve the models.
Work package 3, status: 50-60% completed.
The boulder detection algorithm successfully identified more than 2,000,000 boulders around 82 young and fresh simple impact craters on the Moon and 15 fresh simple impact craters on Mars. The resulting database provides valuable support for both deep learning applications in boulder detection and detailed analyses of ejection patterns. This is the first large-scale database focused specifically on boulders, addressing a significant gap in planetary science compared to the well-established impact crater databases. While the addition of new boulder populations is temporarily on hold to prioritize the preparation of the third manuscript of the BOULDERING project, significant progress is being made in analyzing the existing dataset. The manuscript, aimed at refining our understanding of ejection mechanisms during crater formation, has its methods and results sections completed. Work on the remaining sections is underway, with the goal of submitting the article by March 1, 2025.
