Sensor-agnostic tree species classification using proximal laser scanning (TLS, MLS, ULS) and CNNs 🌳🌲💥🤖

Repository for the work related to the tree species classification using proximally sensed laser scanning data (TLS, MLS, ULS). This work is part of the COST action 3DForEcoTech 3DForEcoTech and co-funded by SmartForest

What is the study case about

The overall aim consists of developing a benchmark dataset for developing new point cloud tree species classification models and benchmarking them

Available data sources (approx. 20K trees 🤯)

Datasets

Below you can find some metadata regarding the available datasets.

dataset_name	n_trees	n_species	data_type	Sensor	acquisition	annotation_quality	forest_type	x	y
wieser_TLS	264	12	TLS	RIEGL VZ-400	15 scans per ha	manual	temperate	14.7073	48.6638
...	...	...	...	...	...	...	...	...	...

Trees

Tree metadata can be found in the tree_metadata_training_publish.csv. Each row represents a single tree with the following fields:

treeID	species	genus	dataset	data_type	tree_H	filename
1	Picea_abies	Picea	wieser_TLS	TLS	15.5	/train/00070.las
...	...	...	...	...	...	...

the "tree_H" variable is simply the difference between the top and bottom z value for each tree and should be used only to get a rough understanding of tree size, however keep in mind that this does NOT necessarily correspond to the real tree height.

Tree distribution by tree species (33 tree species with more than 50 trees)

Tree distribution by genus

Tree height (m) distribution by tree species

Data science competition (Nov 2022 - May 2023) 🏎️

The data science competition will run from Jan 2023 to Apr/May 2023. Each contributor will be able to make a maximum of 3 submissions.

To make a submission should send me (stefano.puliti@nibio.no) a csv file with predictions on the test dataset and with the following two columns:

treeID	predicted_species
523	Pinus_sylvestris
...	...

Leader board 🏁

ranking	Author	Institution	Overall accuracy	Precision	Recall	F1-score	method
1	Julian Frey & Zoe Schindler	University of Freiburg	0.79	0.81	0.79	0.79	DetailView github repo
2	Adrian Straker	University of Goettingen	0.78	0.81	0.78	0.78	YOLOv5 github repo
3	Matt Allen	University of Cambridge	0.76	0.77	0.76	0.76	SimpleView github repo
4	Lukas Winiwarter	UBC/TU Wien	0.76	0.77	0.76	0.75	PointNet++ github repo
5	Nataliia Rehush	WSL	0.74	0.77	0.74	0.73	MinkNet github repo
6	Hristina Hristova & Nataliia Rehush	WSL	0.71	0.72	0.71	0.7	MLP-Mixer github repo
7	Brent Murray	UBC	0.68	0.67	0.68	0.67	PointAugment + DGCNN github repo

Confusion matrix of top performing method 📈