AGBD: A Global-scale Biomass Dataset
- URL: http://arxiv.org/abs/2406.04928v2
- Date: Mon, 09 Dec 2024 11:08:35 GMT
- Title: AGBD: A Global-scale Biomass Dataset
- Authors: Ghjulia Sialelli, Torben Peters, Jan D. Wegner, Konrad Schindler,
- Abstract summary: Existing datasets for Above Ground Biomass estimation from satellite imagery are limited.
This dataset combines AGB reference data from the GEDI mission with data from Sentinel-2 and PALSAR-2 imagery.
It includes pre-processed high-level features such as a dense canopy height map, an elevation map, and a land-cover classification map.
It can be easily accessed using a single line of code, offering a solid basis for efforts towards global AGB estimation.
- Score: 18.976975819550173
- License:
- Abstract: Accurate estimates of Above Ground Biomass (AGB) are essential in addressing two of humanity's biggest challenges, climate change and biodiversity loss. Existing datasets for AGB estimation from satellite imagery are limited. Either they focus on specific, local regions at high resolution, or they offer global coverage at low resolution. There is a need for a machine learning-ready, globally representative, high-resolution benchmark. Our findings indicate significant variability in biomass estimates across different vegetation types, emphasizing the necessity for a dataset that accurately captures global diversity. To address these gaps, we introduce a comprehensive new dataset that is globally distributed, covers a range of vegetation types, and spans several years. This dataset combines AGB reference data from the GEDI mission with data from Sentinel-2 and PALSAR-2 imagery. Additionally, it includes pre-processed high-level features such as a dense canopy height map, an elevation map, and a land-cover classification map. We also produce a dense, high-resolution (10m) map of AGB predictions for the entire area covered by the dataset. Rigorously tested, our dataset is accompanied by several benchmark models and is publicly available. It can be easily accessed using a single line of code, offering a solid basis for efforts towards global AGB estimation. The GitHub repository github.com/ghjuliasialelli/AGBD serves as a one-stop shop for all code and data.
Related papers
- Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework [59.42946541163632]
We introduce a comprehensive geolocation framework with three key components.
GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval, an evaluation metric.
We demonstrate that GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing interpretability.
arXiv Detail & Related papers (2025-02-19T14:21:25Z) - OpenEarthMap-SAR: A Benchmark Synthetic Aperture Radar Dataset for Global High-Resolution Land Cover Mapping [16.387666608029882]
We introduce OpenEarthMap-SAR, a benchmark SAR dataset for global high-resolution land cover mapping.
OpenEarthMap-SAR consists of 1.5 million segments of 5033 aerial and satellite images with the size of 1024$times$1024 pixels, covering 35 regions from Japan, France, and the USA.
We evaluate the performance of state-of-the-art methods for semantic segmentation and present challenging problem settings suitable for further technical development.
arXiv Detail & Related papers (2025-01-18T22:30:27Z) - EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision [72.84868704100595]
This paper presents a dataset specifically designed for self-supervision on remote sensing data, intended to enhance deep learning applications on Earth monitoring tasks.
The dataset spans 15 tera pixels of global remote-sensing data, combining imagery from a diverse range of sources, including NEON, Sentinel, and a novel release of 1m spatial resolution data from Satellogic.
Accompanying the dataset is EarthMAE, a tailored Masked Autoencoder developed to tackle the distinct challenges of remote sensing data.
arXiv Detail & Related papers (2025-01-14T13:42:22Z) - AnySat: An Earth Observation Model for Any Resolutions, Scales, and Modalities [5.767156832161819]
We propose AnySat, a multimodal model based on joint embedding predictive architecture (JEPA) and resolution-adaptive spatial encoders.
To demonstrate the advantages of this unified approach, we compile GeoPlex, a collection of $5$ multimodal datasets.
We then train a single powerful model on these diverse datasets simultaneously.
arXiv Detail & Related papers (2024-12-18T18:11:53Z) - GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial Tasks [84.86699025256705]
We present GEOBench-VLM, a benchmark specifically designed to evaluate Vision-Language Models (VLMs) on geospatial tasks.
Our benchmark features over 10,000 manually verified instructions and covers a diverse set of variations in visual conditions, object type, and scale.
We evaluate several state-of-the-art VLMs to assess their accuracy within the geospatial context.
arXiv Detail & Related papers (2024-11-28T18:59:56Z) - OAM-TCD: A globally diverse dataset of high-resolution tree cover maps [8.336960607169175]
We present a novel open-access dataset for individual tree crown delineation (TCD) in high-resolution aerial imagery sourced from OpenMap (OAM)
Our dataset, OAM-TCD, comprises 5072 2048x2048px images at 10 cm/px resolution with associated human-labeled instance masks for over 280k individual and 56k groups of trees.
Using our dataset, we train reference instance and semantic segmentation models that compare favorably to existing state-of-the-art models.
arXiv Detail & Related papers (2024-07-16T14:11:29Z) - Comparing remote sensing-based forest biomass mapping approaches using new forest inventory plots in contrasting forests in northeastern and southwestern China [6.90293949599626]
Large-scale high spatial resolution aboveground biomass (AGB) maps play a crucial role in determining forest carbon stocks and how they are changing.
GEDI is a sampling instrument, collecting dispersed footprints, and its data must be combined with that from other continuous cover satellites to create high-resolution maps.
We developed local models to estimate forest AGB from GEDI L2A data, as the models used to create GEDI L4 AGB data incorporated minimal field data from China.
arXiv Detail & Related papers (2024-05-24T11:10:58Z) - SatBird: Bird Species Distribution Modeling with Remote Sensing and
Citizen Science Data [68.2366021016172]
We present SatBird, a satellite dataset of locations in the USA with labels derived from presence-absence observation data from the citizen science database eBird.
We also provide a dataset in Kenya representing low-data regimes.
We benchmark a set of baselines on our dataset, including SOTA models for remote sensing tasks.
arXiv Detail & Related papers (2023-11-02T02:00:27Z) - FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From
Multi-Source Optical Imagery [4.9687851703152806]
We introduce the French Land cover from Aerospace ImageRy (FLAIR), an extensive dataset from the French National Institute of Geographical and Forest Information (IGN)
FLAIR contains high-resolution aerial imagery with a ground sample distance of 20 cm and over 20 billion individually labeled pixels for precise land-cover classification.
The dataset also integrates temporal and spectral data from optical satellite time series.
arXiv Detail & Related papers (2023-10-20T07:55:12Z) - Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset,
Benchmarks and Challenges [52.624157840253204]
We present an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points.
Our dataset consists of large areas from three UK cities, covering about 7.6 km2 of the city landscape.
We evaluate the performance of state-of-the-art algorithms on our dataset and provide a comprehensive analysis of the results.
arXiv Detail & Related papers (2020-09-07T14:47:07Z) - Open Graph Benchmark: Datasets for Machine Learning on Graphs [86.96887552203479]
We present the Open Graph Benchmark (OGB) to facilitate scalable, robust, and reproducible graph machine learning (ML) research.
OGB datasets are large-scale, encompass multiple important graph ML tasks, and cover a diverse range of domains.
For each dataset, we provide a unified evaluation protocol using meaningful application-specific data splits and evaluation metrics.
arXiv Detail & Related papers (2020-05-02T03:09:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.