Miti360: An integrated dataset combining remote sensing, ground measurements and weather data for improved reforestation monitoring

Introduction

In the era of artificial intelligence, machine learning combined with remote sensing and ground measurements offers unprecedented opportunities to enhance forest monitoring through faster, more accurate biomass estimation and individual tree analysis. Despite widespread interest, Africa suffers from a shortage of ML-ready forestry datasets, with most major collections—NEON Crowns, Auto Arborist, ReforesTree, and a Northern Australia dataset—originating elsewhere. The Miti360 dataset aims to bridge this geographic gap and is tailored to support data-driven decision-making in establishing and monitoring reforested stands across diverse African landscapes. Existing ML-ready datasets from the Global North have limited relevance in Africa.

Contents

The dataset comprises aerial image data (orthophotos and tiles) annotated with bounding boxes for each tree, annotated terrestrial images (single and stereo), tree inventory data (biophysical parameter measurements, GPS coordinates, and species), and historical weather data (precipitation and temperature). These data were collected from a 770-ha reforested section of the Kieni Forest in Kenya between July 2024 and February 2025.

Below is a tabular summary of the dataset contents:

#	Data Category	Data Type	Quantity	Format
1	Drone Images	Orthophoto	2	TIF
		Tiles	844	TIF
		Tree crown annotations	57058	JSON
		Tree crown species	1208	CSV
		Tree species shapefile	1208	SHP
2	Tree ground measurements	Numeric data	1208 (601 trees in 2024 & 607 trees in 2025)	CSV
3	Ground based single images	Images and tree masks	1208 (601 trees in 2024 & 607 trees in 2025)	JPEG
4	Tree stereo images	Images and tree masks	2416 (601 trees in 2024 & 607 trees in 2025)	JPEG
5	Weather data from 40 stations	Time series data	8 years daily data	API endpoint

For each tree whose data was recorded during the field survey, there is a single image captured using a smartphone and a pair of images captured with a stereo camera. Other attributes recorded are the location, species, height, crown diameter, and basal diameter. These are captured in CSV files with the following column names:

• PHONE_IMAGE_FILENAME: Tree's image taken with a smartphone. Saved in JPG format.
• LEFT_STEREO_IMAGE_FILENAME: Left image of the stereo pair. Saved in JPG format.
• SPECIES: The species of the sampled tree. Given in standard binomial nomenclature.
• TH: Height of the tree in cm.
• CD: Crown diameter of the tree in cm.
• BD: Basal diameter of the tree in cm.
• NORTHINGS: How far north or south, in metres, a tree is from the equator based on the CRS EPSG:21037.
• EASTINGS: How far east or west, in metres, a tree is from the Greenwich meridian based on the CRS EPSG:21037.
• LATITUDE: GPS latitude of the tree in the WGS84 coordinate frame.
• LONGITUDE: GPS longitude of the tree in the WGS84 coordinate frame.

Dataset Organisation and Hosting

• For information on how the dataset is organised, see this README text file.

• The terrestrial and aerial images (together with associated metadata) are hosted in two Google Cloud Storage buckets:

  • Aerial images:
    • Phase one: Contains aerial images collected during phase one.
    • Phase two: Contains aerial images collected during phase two.
  • Terrestrial: Contains ground-based images plus associated files.

• The instructions for accessing the weather data are found in the weather-metadata directory.

Dissemination

• The Miti360 dataset now has a website. You can read more about it and download the dataset from there.

Technical Report

For more details on the methods used to develop the dataset and usage guidelines, please refer to the technical report.

Appropriate Usage of Miti360

Miti360 can be used in varied ways to train and assess machine learning models. One useful research angle we have pursued in the past is that of automating tree inventory using stereoscopic photogrammetry. With recent advances in deep learning and 3D computer vision, the stereoscopic images in Miti360 would be invaluable in developing better techniques for achieving the same goals. Regardless of the ways in which dataset may be used, we believe that all efforts directed towards developing novel techniques for forest monitoring tailored towards our African context will produce the greatest impact.

Licensing

Copyright (C) 2025 Centre for Data Science and Artificial Intelligence, DeKUT

The miti360 dataset is licensed under the CC-BY 4.0 License, Version 4.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at https://creativecommons.org/licenses/by/4.0/legalcode.txt.

Citation

If you use Miti360 for research purposes, please consider citing:

@techreport{kiplimo2024miti360,
  title = {Miti360: An Integrated Dataset Combining Remote Sensing, Ground Measurements and Weather Data for Improved Reforestation Monitoring},
  author = {Kiplimo, Cedric and Mbatia, Samuel and wa Maina, Ciira and Sichangi, Arthur and Gitundu, Denis},
  institution = {Centre for Data Science and Artificial Intelligence (DSAIL), Dedan Kimathi University of Technology},
  year = {2024},
  address = {Nyeri, Kenya}
}

Contact Information

Cedric Kiplimo: cedric.kiplimo@dkut.ac.ke or dsail-info@dkut.ac.ke