iSAID contains 655,451 object instances for 15 categories across 2,806 high-resolution images. The images of iSAID is the same as the DOTA-v1.0 dataset, which are manily collected from the Google Earth, some are taken by satellite JL-1, the others are taken by satellite GF-2 of the China Centre for Resources Satellite Data and Application.

The authors of the iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images dataset have introduced the first benchmark dataset for instance segmentation in aerial imagery, which merges instance-level object detection and pixel-level segmentation tasks. It contains 655,451 object instances spanning 15 different categories across 2,806 high-resolution images. Precise per-pixel annotations have been provided for each instance, ensuring accurate localization for detailed scene analysis. Compared to existing small-scale aerial image-based instance segmentation datasets, iSAID boasts 15 times the number of object categories and 5 times the number of instances.

Context

Existing Earth Vision datasets are either suitable for semantic segmentation or object detection. iSAID is the first benchmark dataset for instance segmentation in aerial images. This large-scale and densely annotated dataset contains 655,451 object instances for 15 categories across 2,806 high-resolution images. The distinctive characteristics of iSAID are the following: (a) large number of images with high spatial resolution, (b) fifteen important and commonly occurring categories, (c) large number of instances per category, (d) large count of labelled instances per image, which might help in learning contextual information, (e) huge object scale variation, containing small, medium and large objects, often within the same image, (f) Imbalanced and uneven distribution of objects with varying orientation within images, depicting real-life aerial conditions, (g) several small size objects, with ambiguous appearance, can only be resolved with contextual reasoning, (h) precise instance-level annotations carried out by professional annotators, cross-checked and validated by expert annotators complying with well-defined guidelines.

Content

The images of iSAID is the same as the DOTA-v1.0 dataset, which are manily collected from the Google Earth, some are taken by satellite JL-1, the others are taken by satellite GF-2 of the China Centre for Resources Satellite Data and Application.

Use of the images from Google Earth must respect the corresponding terms of use: "Google Earth" terms of use.

All images and their associated annotations in iSAID can be used for academic purposes only, but any commercial use is prohibited.

Object Category The object categories in iSAID include: plane, ship, storage tank, baseball diamond, tennis court, basketball court, ground track field, harbor, bridge, large vehicle, small vehicle, helicopter, roundabout, soccer ball field and swimming pool.

Annotation format The iSAID uses pixel-level annotations. Each pixel represents a particular class. The annotation follows the format of MS COCO.

Acknowledgements

@inproceedings{waqas2019isaid, title={iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images}, author={Waqas Zamir, Syed and Arora, Aditya and Gupta, Akshita and Khan, Salman and Sun, Guolei and Shahbaz Khan, Fahad and Zhu, Fan and Shao, Ling and Xia, Gui-Song and Bai, Xiang}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops}, pages={28--37}, year={2019} }

@InProceedings{Xia_2018_CVPR, author = {Xia, Gui-Song and Bai, Xiang and Ding, Jian and Zhu, Zhen and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei}, title = {DOTA: A Large-Scale Dataset for Object Detection in Aerial Images}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2018} }

ISAID

## Overview

ISAID is a dataset for instance segmentation tasks - it contains Plane annotations for 9,436 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

512).

Each abbreviation is explained as: SV(Small vehicle), BD(Baseball diamond), HC(Helicopter), SP(Swimming pool), TC(Tennis court), LV(Large vehicle), SC(Storage tank), GTF(Ground field track), SBF(Soccer-ball field), BC(Basketball court), and RA(Roundabout).

This is Version 6.0, 20200130 of the catalog of publicly available USAID datasets

USAID - India

The best results in the experiments are indicated by the values in bold in each column.

This repository contains the data description and processing for the paper titled "SkySense++: A Semantic-Enhanced Multi-Modal Remote Sensing Foundation Model for Earth Observation." The code is in here

🔥🔥🔥 Last Updated on 2025.03.14 🔥🔥🔥

• [2025.3.14] updated optical images of JL-16 dataset (https://huggingface.co/datasets/KKKKKKang/JL-16)
• [2025.3.12] updated sentinel-1 images and labels of JL-16 dataset
• [2025.3.9] updated pretrain and evaluation data

We conduct semantic-enhanced pretraining on the RS-Semantic dataset, which consists of 13 datasets with pixel-level annotations. Below are the specifics of these datasets.

Dataset	Modalities	GSD(m)	Size	Categories	Download Link
Five Billion Pixels	Gaofen-2	4	6800x7200	24	Download
Potsdam	Airborne	0.05	6000x6000	5	Download
Vaihingen	Airborne	0.05	2494x2064	5	Download
Deepglobe	WorldView	0.5	2448x2448	6	Download
iSAID	Multiple Sensors	-	800x800 to 4000x13000	15	Download
LoveDA	Spaceborne	0.3	1024x1024	7	Download
DynamicEarthNet	WorldView	0.3	1024x1024	7	Download
	Sentinel-2*	10	32x32
	Sentinel-1*	10	32x33
Pastis-MM	WorldView	0.3	1024x1024	18	Download
	Sentinel-2*	10	32x32
	Sentinel-1*	10	32x33
C2Seg-AB	Sentinel-2*	10	128x128	13	Download
	Sentinel-1*	10	128x128
FLAIR	Spot-5	0.2	512x512	12	Download
	Sentinel-2*	10	40x40
DFC20	Sentinel-2	10	256x256	9	Download
	Sentinel-1	10	256x256
S2-naip	NAIP	1	512x512	32	Download
	Sentinel-2*	10	64x64
	Sentinel-1*	10	64x64
JL-16	Jilin-1	0.72	512x512	16	Download
	Sentinel-1*	10	40x40

* for time-series data.

We evaluate our SkySense++ on 12 typical Earth Observation (EO) tasks across 7 domains: agriculture, forestry, oceanography, atmosphere, biology, land surveying, and disaster management. The detailed information about the datasets used for evaluation is as follows.

Domain	Task type	Dataset	Modalities	GSD	Image size	Download Link	Notes
Agriculture	Crop classification	Germany	Sentinel-2*	10	24x24	Download
Foresetry	Tree species classification	TreeSatAI-Time-Series	Airborne,	0.2	304x304	Download
			Sentinel-2*	10	6x6
			Sentinel-1*	10	6x6
	Deforestation segmentation	Atlantic	Sentinel-2	10	512x512	Download
Oceanography	Oil spill segmentation	SOS	Sentinel-1	10	256x256	Download
Atmosphere	Air pollution regression	3pollution	Sentinel-2	10	200x200	Download
			Sentinel-5P	2600	120x120
Biology	Wildlife detection	Kenya	Airborne	-	3068x4603	Download
Land surveying	LULC mapping	C2Seg-BW	Gaofen-6	10	256x256	Download
			Gaofen-3	10	256x256
	Change detection	dsifn-cd	GoogleEarth	0.3	512x512	Download
Disaster management	Flood monitoring	Flood-3i	Airborne	0.05	256 × 256	Download
		C2SMSFloods	Sentinel-2, Sentinel-1	10	512x512	Download
	Wildfire monitoring	CABUAR	Sentinel-2	10	5490 × 5490	Download
	Landslide mapping	GVLM	GoogleEarth	0.3	1748x1748 ~ 10808x7424	Download
	Building damage assessment	xBD	WorldView	0.3	1024x1024	Download

* for time-series data.