PANGAEA_993969 HeideBench: A Multispectral UAV Time-Series Benchmark for Forest Crown Phenology in Dölauer Heide Khan, Taimur HeideBench: A Multispectral UAV Time-Series Benchmark for Forest Crown Phenology in Dölauer Heide 2026-05-19 PANGAEA Dataset https://doi.pangaea.de/10.1594/PANGAEA.993969 Investigator Taimur Khan Earth Science Date Raster graphic, GeoTIFF format File content Coordinate reference system UTM Easting, Universal Transverse Mercator UTM Northing, Universal Transverse Mercator Longitude, westbound Latitude, southbound Longitude, eastbound Latitude, northbound Pixel size Image pixel width Image pixel height Comment Description Feature counts Date/time start Date/time end Duration Solar elevation angle Solar azimuth angle Indicator geoscientificInformation Drone, DJI, Mavic3M Forest HeideBench HeideBench_2025 Multispectral Image Data phenology Timeseries UAV 2025-03-06 2025-11-05 Complete 51.499959 51.508576 11.902653 11.911325 unrestricted CC-BY-4.0: Creative Commons Attribution 4.0 International English PANGAEA Data Publisher for Earth & Environmental Science https://www.pangaea.de/ Data Center Contact Michael Diepenbroek info@pangaea.de

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Bremen Bremen 28359 Germany online 492 data points text/tab-separated-values Albrecht, T; Buschendorf, J; Drechsler, K; Geissler, H; Gluch, W; Gnielka, R; Weidlich, M (1993): Die Dölauer Heide-Waldidylle in Großstadtnähe. Khan, Taimur; Arnold, Caroline; Grover, Harsh (2025): DeepTrees: Tree Crown Segmentation and Analysis in Remote Sensing Imagery with PyTorch. Journal of Open Source Software, 10(114), 8056, https://doi.org/10.21105/joss.08056 ReadMe - file description of Multispectral UAV Time-Series Benchmark dataset from Heidebench 2025 (URI: https://download.pangaea.de/reference/139842/attachments/README_v2.md)

We present HeideBench, a very-high-resolution multispectral uncrewed aerial vehicle dataset for forest crown phenology collected over a forest patch in Dölauer Heide, Halle (Saale), Germany. Dölauer Heide is currently dominated by pine plantations (Kiefernforste), which cover the largest area but are increasingly affected by dieback, while its potential natural vegetation is sessile oak–hornbeam forest rich in small-leaved lime (Albrecht et al., 1993). In addition to these pine stands, the area contains near-natural mixed deciduous forests with oaks, birches, and beeches, making it a particularly relevant setting for observing seasonal canopy development under contrasting forest structures and ongoing ecological transition. Against this background, HeideBench provides repeated observations of the same forest patch through the growing season. The dataset contains 18 georeferenced multispectral GeoTIFF orthomosaics acquired between 6 March 2025 and 5 November 2025, spanning a 244-day seasonal period from early spring to late autumn. The acquisitions have a median revisit interval of 14 days, with intervals ranging from 4 to 27 days, and an average ground sampling distance of 5.53 cm per pixel. The valid imaging footprint covers approximately 32.1 ha and is bounded by 11.902653–11.911325°E and 51.499959–51.508576°N. Data were collected using a DJI Mavic 3M Enterprise uncrewed aerial vehicle equipped with four multispectral cameras measuring green (560 nm), red (650 nm), red-edge (730 nm), and near-infrared (860 nm) reflectance, in that order. Flights used a real-time kinematic (RTK) positioning module for centimeter-level geolocation, and all data are provided in coordinate reference system EPSG:25832. Imagery was processed with Agisoft Metashape 2.3.1 to generate calibrated multispectral orthomosaics. The dataset further includes 5,885 crop-safe individual tree crown instance segmentations over the same footprint, extracted with the DeepTrees software package (Khan et al., 2025). HeideBench is intended to support crown-centric analyses of seasonal canopy development, temporal representation learning, phenology-aware feature extraction, and the evaluation of tree crown delineation under seasonal change. HeideBench is a result of the Dynamic Platform Project titled "PhenoEmbed: Multispectral UAV AI Embeddings for phenology-aware tree crown delineation" of the Integration Platform 1: "Sustainable future land use" (IP1) at the Helmholz Centre for Environmental Research (UFZ) in Leipzig, Germany. ** For all details see the full metadata description at "https://doi.pangaea.de/10.1594/PANGAEA.993969"!

http://en.wikipedia.org/wiki/ISO_8601 Date http://en.wikipedia.org/wiki/ISO_8601 Date/time end http://en.wikipedia.org/wiki/ISO_8601 Date/time start https://doi.org/10.21105/joss.08056 DeepTrees: Tree Crown Segmentation and Analysis in Remote Sensing Imagery with PyTorch https://enterprise.dji.com/mavic-3-m Drone, DJI, Mavic3M DIF 9.4 2026-05-19 2026-05-19