Image Segmentation Datasets

TreeUQ is a large-scale Earth observation benchmark for tree species mapping and tree structure estimation (height, count, density, variance) over Bavaria, Germany. Each 128×128 pixel patch (10 m resolution, EPSG:25832) contains: - Sentinel-2 — 4 seasonal composites (spring/summer/autumn/winter 2025), 10 bands - Sentinel-1 GRD — 4 seasonal composites, VV + VH polarisation, linear gamma-0 - Tree species raster — Bavarian species classification aligned to the 10 m grid - Supervision labels — per-pixel height, count, density from the Bavarian Einzelbäume individual-tree inventory - DOP20 aerial imagery (optional) — 20 cm RGB orthophotos as 6400×6400 chips Splits (spatial blocks): train 31,806…

Pre-computed High-Dimensional Forensic Latents for ultra-fast Diffusion-Diff forensics training. - imgid (string): Image identifier. - label (int64): Class label (0: authentic, 1: fully synthetic, 2: tampered/inpainted). - zhighdim (binary): Pre-computed 84-channel fp16 latent tensor bytes. - mask (binary): Ground-truth binary manipulation mask encoded as PNG bytes. - channels (int64): Total channel count (84). - latenth (int64): Latent height (64). - latentw (int64): Latent width (64).

Dataset · Image segmentation

cd-ogi-recording-mask-audit

Sankalp Thakur

Audit tables for a small gas-imaging study on baselab's Controlled Diet dataset, which is laboratory methane from rumen fermentation. Its 137 rows describe 42 source images from nine recordings. They are not images, chemical measurements or gas-free sessions. The spatial prior's comparison IoU is 0.138078, against 0.026416 for the tiny MLP and 0.022555 for logistic regression: both learned models lose. Empty cells are undefined metrics, not zeros. backgroundpixeloverpredictionratio measures pixel overprediction against weak masks. It is never a gas false-alarm rate. Models, weights and the full report are in by Mohamed G. Embaby, Toqi Tahamid Sarker, Amer AbuGhazaleh and Khaled R. Ahmed…

Dataset · Image segmentation

ocean-eddy-datasets

Bingzheng Yan

Figure 1. Visualization example from the SCSE-Eddy dataset. The left panel shows SSH intensity, and the right panel shows the corresponding ground-truth eddy segmentation label. This repository provides ocean eddy datasets for semantic segmentation and a curated PredCR candidate-level instance package for reliability reasoning and concept-based analysis. The datasets are designed for pixel-level identification of mesoscale ocean eddies using deep learning methods. They are mainly used for experiments involving EddyNet, DUNet-like segmentation baselines, and future neuro-symbolic verification and correction frameworks. The repository currently contains two ocean eddy segmentation datasets…

Dataset · Image segmentation

deepglobe-roads

Lam Tran

The DeepGlobe Road Extraction Dataset consists of 6,226 optical satellite images with a spatial dimension of $1024 \times 1024$ pixels and a Ground Sampling Distance (GSD) of 0.5 m/pixel. The imagery spans varied urban, suburban, and rural terrains across Thailand, Indonesia, and India. This repository hosts the standardized, reproducible benchmark partition: Partitioning is constructed deterministically with seed 42 from the sorted filename collection. If you use this dataset in your research, please cite the original challenge paper

Pre-computed High-Dimensional Forensic Latents for ultra-fast Diffusion-Diff forensics training. - imgid (string): Image identifier. - label (int64): Class label (0: authentic, 1: fully synthetic, 2: tampered/inpainted). - zhighdim (binary): Pre-computed 256-channel fp16 latent tensor bytes. - mask (binary): Ground-truth binary manipulation mask encoded as PNG bytes. - channels (int64): Total channel count (256). - latenth (int64): Latent height (32). - latentw (int64): Latent width (32).

Automated detection of expressway pavement cracks is critically important for infrastructure inspection and maintenance. In this task, range information provides a key geometric cue for distinguishing genuine cracks from pavement textures, shadows, stains, and other visual interference. However, industrial-grade pavement data acquisition equipment is expensive, and pixel-wise annotation is labor intensive. Consequently, most existing pavement crack datasets contain only visual imagery and cannot precisely represent the geometric morphology of the pavement surface. GTCrack was developed to alleviate this data bottleneck. A survey vehicle equipped with two laser line profilers was used to…

Dataset · Image segmentation

massachusetts-roads

Lam Tran

High-resolution aerial imagery benchmark for remote sensing road extraction, based on the canonical Massachusetts Roads dataset introduced by Volodymyr Mnih (2013). The Massachusetts Roads Dataset covers diverse urban, suburban, coastal, and forested rural environments throughout the Commonwealth of Massachusetts. Standard benchmark partition for road extraction research: If you use this dataset in your research, please cite

Dataset · Image segmentation

weed_segmentation_france

AgML

This dataset comprises real RGB images of weeds in agricultural fields at Montoldre, France, specifically within maize and bean crop areas. Captured using a robot platform during 2019 and 2021, the images were collected under natural field conditions to support semantic segmentation research for weed detection in crop environments. The dataset contains 985 images with pixel-level mask annotations. This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. This dataset was reformatted from its original format to match HuggingFace standards.