DOI: 10.5281/zenodo.14973741
title: Lunar Landing Dataset

description: This dataset was synthetically generated as part of a Master’s thesis in Space Engineering. It is based on Digital Elevation Model (DEM) data from the Lunar Reconnaissance Orbiter (LRO) mission, specifically the SLDEM - Selene/LRO Digital Elevation Model at 60 m/pixel resolution. The dataset spans longitudes from 0° to 360°E and latitudes from -60° to 60°N, subdivided into 32 tiles of 30° latitude by 45° longitude each. Surface textures were derived from the LROC WAC Hapke Normalized Mosaic, ensuring photometric consistency.
description: The dataset was generated using Python-based scripts leveraging the open-source Blender software. The CORTO (Celestial Object Rendering TOol) framework, developed at the DART Lab, Politecnico di Milano, was employed for high-fidelity synthetic rendering. CORTO enables both offline dataset generation and online closed-loop simulations, making it suitable for vision-based spacecraft navigation applications.
description: Geometric Variability:To simulate realistic spacecraft observations, each render is generated with randomized geometric parameters, ensuring a diverse dataset for generalization across different lunar perspectives. Key parameters include:



Altitude: 20–120 km

Latitude & Longitude: -60° to 60°, 0° to 360°

Azimuth Angle: 0° to 360°

Elevation Angle: 30° to 90°

Sun Inclination: 5° to 15°

Sun Azimuth: -45° to 45°


Tile Distribution:The lunar surface is divided into 5° × 5° tiles (longitude × latitude) to systematically cover diverse terrains. Each tile undergoes multiple renderings under different conditions, resulting in a dataset of 21,144 high-fidelity synthetic images, distributed as follows:



Total Number of Tiles: 1,762

Renders per Tile: 12


Rendering Configuration & Computational Tradeoffs:Rendering is performed using Blender’s Cycles engine with physically accurate lighting and shading models. Given the need for efficient generation of over 20,000 images, the following optimizations were implemented:



Reduced Sampling Rate: 4 samples per render, balancing speed and crater detection quality.

Optimized Resolution: 512 × 512 pixels for computational efficiency.

Filmic View Transform: Enhancing contrast while preserving grayscale dynamic range.

CPU-Based Multi-Threading: Enabling parallelized batch rendering.

Exposure & Energy Scaling: Adjusted based on tile dimensions for consistency.


Final rendering parameters:



Field of View (FOV): 75°

Resolution: 512 × 512 pixels

Film Exposure: 10

Color Band: Black & White

Bit Encoding: 8-bit

View Transform: Filmic

Sun Angle: 0.53°

Sun Energy: 10 (tile-dependent)

Render Engine: Cycles

Rendering Device: CPU

Samples per Render: 4


This dataset provides a comprehensive, high-fidelity representation of the lunar surface, supporting the development of robust vision-based navigation algorithms.
description: Dataset Folder Structure

The dataset is organized into multiple subfolders, each containing specific data types to support supervised learning and synthetic lunar surface analysis.

1. img/



This folder contains the synthetic lunar surface renders generated using Blender and the CORTO framework.

Images are provided in 512 × 512 pixel resolution and rendered using physically accurate shading and lighting models.


2. crater_masks_png/



Contains crater masks overlaid on the original renders in PNG format.

The crater annotations are based on the Robbins Crater Database, ensuring accuracy in crater locations and dimensions.


3. YOLO1_labels/



Provides YOLO-compatible segmentation labels for craters.

Each crater is annotated using a series of normalized pixel coordinates forming an elliptical contour.

Format: ⟨class-index⟩ x1 y1 x2 y2 ... xn yn



class-index: Represents the crater category (0 = crater).

xi, yi: Normalized pixel coordinates of the crater rim.




4. YOLO2_labels/



Extends YOLO1_labels by including both segmentation and size classification information.

Enables supervised learning models to differentiate craters based on their scale.

Format: ⟨class-index⟩ x1 y1 x2 y2 ... xn yn



class-index: Represents the crater’s size category (0 = small, 1 = medium, 2 = large).

xi, yi: Normalized pixel coordinates of the crater rim.




5. altimeter/



Contains TXT files simulating altimeter measurements in kilometers.

These values represent the estimated altitude of the spacecraft above the lunar surface.


6. depth_txt/



Provides TXT files containing a pixel-wise depth map in kilometers.

Each value represents the elevation of the lunar surface at that specific pixel.


7. lat_lon_exr/



Contains OpenEXR (32-bit floating point, RGBA) files storing the pixel-wise coordinates in the lunar reference frame.

Channels:



R channel: Stores latitude values.

G channel: Stores longitude values.