DeepCraters

Lunar impact craters identification and age estimation with Chang'E data by deep and transfer learning

DeepCraters is a pipeline for training a convolutional neuralnetwork (CNN) to identify impact craters on the Moon and training a dual-channel impact crater age estimation model to classify the impact craters identified before.

Getting Started

Overview

The DeepCraters pipeline trains a impact craters identified model and age estimatiom model using data derived from CE-1 and CE-2 DOM and DEM image data and catalogue of craters. The code is divided into two parts. The first trains a impact craters identified model with R-FCN [the details can be found in the subfile craters_detection/]; The second trains a dual-channel impact crater classification model using craters with constrained ages and craters detected without age.

Data Sources

I. http://moon.bao.ac.cn/ [CE-1 and CE-2 DOM and DEM data can be obtained from this website]
II. https://planetarynames.wr.usgs.gov/Page/MOON/target [recognized lunar impact craters regulated by the IAU can be obtained from this website]
III. https://www.lpi.usra.edu/lunar/surface/ [lunar craters with constrained ages aggregated by the LPI can be obtained from this website]
IV. https://astrogeology.usgs.gov/search/map/Moon/Geology/Lunar_Geologic_GIS_Renovation_March2013 [Lunar 1:5M Geologic Map, 38 stratigraphic information of craters extracting from, can be obtained from this website]

NOTE: All the craters with constrained ages in 'II' are contained in 'III'.

The craters used for 'ctaters detection' can be find in /craters_detection/data_list/, and the craters with constrained ages used for 'age estimatiom' can be find in /age_estimation/data_list/.

Running DeepCraters

Each part of DeepCraters has the corresponding script:

part 1 (craters_detection):
RFCN_ROOT/experiments/scripts/moon_rfcn_end2end.sh for build and train the detection model,
RFCN_ROOT/tools/rfcn_test_Moon_Detect.py to generate a crater atlas of study area.
The more details can be find in README for Craters Detection.
part 2 (age_estimation):
train_moon_age_estimation.py for build and train the age estimation model,
pred_dete_moon_age_estimation.py to predict the age of all craters detected in part 1.
The more details can be find in README for Age Estimation.

We recommend copying these scripts into a new working directory (and appending this repo to your Python path) instead of modifying them in the repo.

License