IntegraCAR-LULC-500: A High-Resolution Optical Satellite Dataset for LULC Segmentation in the Brazilian Rural Environmental Registry

Dataset Summary

IntegraCAR-LULC-500 is a high-resolution remote sensing dataset designed for Land-Use and Land-Cover (LULC) semantic segmentation in the context of the Brazilian Rural Environmental Registry (Cadastro Ambiental Rural - CAR). Developed by researchers at the Instituto Federal do Espírito Santo (IFES) in collaboration with the Instituto de Defesa Agropecuária e Florestal do Espírito Santo (IDAF), this dataset is published in conjunction with our SIBGRAPI 2026 paper:

IntegraCAR-LULC-10K: A High-Resolution Optical Satellite Dataset for LULC Segmentation in the Brazilian Rural Environmental Registry
Calebe Albertino, Gabriel M. B. Lima, Vinícius R. Oliveira, Arthur R. V. Ribeiro, Eliza K. S. Oliveira, Eduardo H. P. Souza, Otavio G. Dalvi, Karin S. Komati, Jefferson O. Andrade, Francisco A. Boldt, and Thiago M. Paixão. (SIBGRAPI 2026)

This repository contains the 500-sample geographically stratified benchmark subset (IntegraCAR-LULC-500) extracted from the larger 10,000-tile collection (IntegraCAR-LULC-10K). It comprises $2048\times 20482048\; \backslash times\; 2048$ pixel image tiles with spatial resolution of $0.5\text{ m/pixel}0.5\backslash text\{\; m/pixel\}$, paired with high-quality pixel-level thematic annotations derived from official state land-cover mappings.

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Data Acquisition & Extraction Pipeline

Data acquisition is based on publicly available geospatial layers from the GeoBases platform (https://geobases.es.gov.br/), reflecting orthophotomosaics and thematic maps created by specialists from the Jones dos Santos Neves Institute (IJSN) through visual photo-interpretation of KOMPSAT-3/3A optical satellite imagery ( $0.5\text{ m/pixel}0.5\backslash text\{\; m/pixel\}$ ) collected between 2019 and 2020.

To replace slow and manual QGIS-based data retrieval, we developed a fully automated custom API workflow:

1. Sampling Strategy: Coordinates were sampled from an official registry database of CAR rural properties across the state of Espírito Santo (ES), Brazil.
2. Geographic Stratification: To ensure representative sampling across diverse ecological and agricultural zones, coordinates were stratified across the 10 official micro-regions defined by the Secretariat for Development of Espírito Santo (SEDES).
3. Automated Querying: For each coordinate, our custom API queries GeoBases Web Map Services (WMS) to download centered $2048\times 20482048\; \backslash times\; 2048$ pixel image tiles (covering $1,024\text{ m}\times 1,024\text{ m}1,024\backslash text\{\; m\}\; \backslash times\; 1,024\backslash text\{\; m\}$ on the ground) paired with shapefile thematic maps rasterized into segmentation masks.

The IntegraCAR-LULC-500 benchmark contains 50 samples per micro-region, split into:

• 300 Training tiles (30 per micro-region)
• 100 Validation tiles (10 per micro-region)
• 100 Testing tiles (10 per micro-region)

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Semantic Class Taxonomy

The reference maps from IJSN originally feature 15 base categories and 24 fine-grained subcategories. In consultation with IDAF environmental management specialists, we grouped these into five CAR-oriented thematic classes:

Class ID	Class Name	Description	Original GeoBases Categories	Portuguese Term
0	Vegetation Areas	Native forests, wetlands, mangroves, marshes, restinga, and high-altitude rupestrian grasslands.	2, 4, 5, 6, 7, 8	Áreas de Vegetação
1	Agropastoral Areas	Pasturelands, cultivated fields (coffee, sugarcane, papaya, banana, etc.), prepared agricultural land, and regeneration areas.	1, 3	Áreas Agropastoris
2	Infrastructure	Houses, buildings, paved roads, dirt roads, sidewalks, mineral extraction sites, and constructed surfaces.	10, 11, 12, 13, 15	Infraestrutura
3	Water Bodies	Rivers, streams, lagoons, reservoirs, and visible surface-water bodies.	14	Corpos d'Água
4	Macega	Unmanaged or transitional vegetation generally composed of dense, coarse, dry tall grasses and tangled shrubs in uncultivated areas.	9	Macega

Note on Macega: Macega was preserved as a standalone class due to its distinct ecological and institutional significance in rural land monitoring, representing land in transition that is neither consolidated native forest nor actively managed agropastoral land.

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Dataset Structure & Features

The dataset is hosted on Hugging Face as a DatasetDict containing distinct splits for satellite tiles (satellite_*) and segmentation ground-truth masks (mask_*):

DatasetDict({
    mask_train: Dataset({ features: ['image', 'filename', 'latitude', 'longitude', 'municipio', 'microestad'], num_rows: 300 }),
    mask_val: Dataset({ features: ['image', 'filename', 'latitude', 'longitude', 'municipio', 'microestad'], num_rows: 100 }),
    mask_test: Dataset({ features: ['image', 'filename', 'latitude', 'longitude', 'municipio', 'microestad'], num_rows: 100 }),
    satellite_train: Dataset({ features: ['image', 'filename', 'latitude', 'longitude', 'municipio', 'microestad'], num_rows: 300 }),
    satellite_val: Dataset({ features: ['image', 'filename', 'latitude', 'longitude', 'municipio', 'microestad'], num_rows: 100 }),
    satellite_test: Dataset({ features: ['image', 'filename', 'latitude', 'longitude', 'municipio', 'microestad'], num_rows: 100 })
})

Feature Definitions

• image (PIL.Image.Image): High-resolution tile ( $2048\times 20482048\; \backslash times\; 2048$ pixels, RGBA format for satellite imagery; single-channel/RGB mask representation for segmentation masks).
• filename (str): Unique identifier formatted by CAR property code (e.g., ES-3200102-36D5A14EF3224FC99B38DB4E06604494.tif).
• latitude (float): SIRGAS 2000 UTM projected Y-coordinate of the tile center.
• longitude (float): SIRGAS 2000 UTM projected X-coordinate of the tile center.
• municipio (str): Municipality name in Espírito Santo (e.g., Afonso Claudio).
• microestad (str): Official SEDES micro-region name (e.g., Sudoeste Serrana).

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Getting Started & Python Usage

You can easily load the dataset using the Hugging Face datasets library and pair the satellite images with their corresponding segmentation masks:

from datasets import load_dataset
import numpy as np
from PIL import Image
import torch
from torch.utils.data import Dataset, DataLoader

# 1. Load the dataset from Hugging Face
ds = load_dataset("laicsiifes/IntegraCAR-LULC-500")

# 2. PyTorch Dataset implementation for paired loading
class IntegraCARDataset(Dataset):
    def __init__(self, dataset_dict, split="train", transform=None):
        self.sat_ds = dataset_dict[f"satellite_{split}"]
        self.mask_ds = dataset_dict[f"mask_{split}"]
        self.transform = transform
        
        # Verify alignment between satellite tiles and mask ground truths
        assert len(self.sat_ds) == len(self.mask_ds), "Split sizes do not match!"

    def __len__(self):
        return len(self.sat_ds)

    def __getitem__(self, idx):
        sat_item = self.sat_ds[idx]
        mask_item = self.mask_ds[idx]
        
        # Ensure filenames match
        assert sat_item["filename"] == mask_item["filename"], f"Mismatch at index {idx}"
        
        # Extract PIL images
        sat_img = sat_item["image"].convert("RGB") # 2048 x 2048 RGB
        mask_img = np.array(mask_item["image"])     # Pixel class IDs [0..4]
        
        metadata = {
            "filename": sat_item["filename"],
            "municipio": sat_item["municipio"],
            "microregion": sat_item["microestad"],
            "coords": (sat_item["latitude"], sat_item["longitude"])
        }
        
        if self.transform:
            sat_img, mask_img = self.transform(sat_img, mask_img)
            
        return sat_img, mask_img, metadata

# Example Usage:
train_dataset = IntegraCARDataset(ds, split="train")
sat_img, mask, meta = train_dataset[0]

print(f"Loaded sample: {meta['filename']}")
print(f"Location: {meta['municipio']} ({meta['microregion']})")
print(f"Satellite Image shape: {np.array(sat_img).shape}")
print(f"Mask shape: {mask.shape}, Unique classes present: {np.unique(mask)}")

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Experimental Baselines (SIBGRAPI 2026)

We evaluated standard deep learning architectures for semantic segmentation on IntegraCAR-LULC-500 using patch extraction with sliding window inference:

• U-Net equipped with an EfficientNet-B5 encoder ( $256\times 256256\; \backslash times\; 256$ input resolution).
• DeepLabv3 equipped with a ResNet-50 encoder ( $512\times 512512\; \backslash times\; 512$ input resolution).

Quantitative Benchmark Results

Model	Encoder	Input Patch	Step (px)	Overlap (%)	Accuracy (%)	Macro F1 (%)	mIoU (%)	Training Time (h)
U-Net	EfficientNet-B5	${256}^{2}256^2$	32	87.5%	78.0	61.3	47.8	118.3
U-Net	EfficientNet-B5	${256}^{2}256^2$	64	75.0%	82.5	62.7	53.7	42.6
U-Net	EfficientNet-B5	${256}^{2}256^2$	128	50.0%	74.0	56.8	43.1	19.7
U-Net	EfficientNet-B5	${256}^{2}256^2$	256	0.0%	74.4	65.5	48.6	3.3
DeepLabv3	ResNet-50	${512}^{2}512^2$	64	87.5%	82.5	66.8	53.7	50.6
DeepLabv3	ResNet-50	${512}^{2}512^2$	128	75.0%	81.6	65.8	52.6	14.1
DeepLabv3	ResNet-50	${512}^{2}512^2$	256	50.0%	80.9	64.3	50.8	4.2
DeepLabv3	ResNet-50	${512}^{2}512^2$	512	0.0%	81.0	65.5	52.0	1.5

Experiments executed on NVIDIA H200 NVL GPU with PyTorch 2.x.

Per-Class Metrics (DeepLabv3, 64-px Step)

Class Name	IoU (%)	Precision (%)	Recall (%)	F1-Score (%)	Class Frequency (%)
Agropastoral Areas	80.1	86.8	91.2	88.9	54.5%
Vegetation Areas	75.4	83.1	89.1	86.0	27.7%
Water Bodies	50.6	75.9	60.3	67.2	1.2%
Infrastructure	44.1	64.6	58.2	61.2	10.6%
Macega	18.2	48.6	22.6	30.8	6.0%

Key Findings

1. Dominant Classes: Models accurately segment consolidated Agropastoral Areas ( $80.1\mathrm{\%}80.1\backslash \%$ IoU) and Vegetation Areas ( $75.4\mathrm{\%}75.4\backslash \%$ IoU).
2. Transitional Class Challenge: Macega remains highly challenging ( $18.2\mathrm{\%}18.2\backslash \%$ IoU) due to strong visual, spectral, and textural overlap with surrounding agropastoral pastures and unmanaged fields.
3. Spectral Constraints: RGB imagery exhibits limitations in distinguishing narrow water channels obscured by canopy cover and distinguishing fragmented dirt infrastructure.

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Dataset Limitations

• Photo-Interpretation Ground Truth: Labels are based on visual photo-interpretation of optical satellite scenes. Boundary uncertainties may exist in shadowed, occluded, or complex transitional zones.
• RGB Only: Imagery is restricted to RGB bands ( $0.5\text{ m}0.5\backslash text\{\; m\}$ spatial resolution). Incorporating Near-Infrared (NIR) or multi-temporal satellite series (e.g., CBERS-4A / Sentinel-2) is recommended for fine-grained discrimination of water bodies and vegetation dynamics.
• Temporal Window: Mappings correspond to the 2019–2020 temporal window provided by GeoBases.

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Citation Information

If you use IntegraCAR-LULC-500 or the IntegraCAR-LULC-10K acquisition pipeline in your research, please cite our SIBGRAPI 2026 paper:

@inproceedings{integracar_lulc_2026,
  title     = {IntegraCAR-LULC-10K: A High-Resolution Optical Satellite Dataset for LULC Segmentation in the Brazilian Rural Environmental Registry},
  author    = {Albertino, Calebe and Lima, Gabriel M. B. and Oliveira, Vin{\'{i}}cius R. and Ribeiro, Arthur R. V. and Oliveira, Eliza K. S. and Souza, Eduardo H. P. and Dalvi, Otavio G. and Komati, Karin S. and Andrade, Jefferson O. and Boldt, Francisco A. and Paix{\~{a}}o, Thiago M.},
  booktitle = {Proceedings of the 39th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)},
  year      = {2026}
}

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Acknowledgments

This research is supported by:

• Instituto Federal do Espírito Santo (IFES) – Serra, ES, Brazil.
• Instituto de Defesa Agropecuária e Florestal do Espírito Santo (IDAF) – Vitória, ES, Brazil.
• FAPES (Grant No. 1048/2025, Project DI 016/2025 - IntegraCAR: Integração do Cadastro Ambiental Rural no Estado do Espírito Santo; Grant 1023/2022, 055/2026, 374/2026).
• CNPq (DT-2 Grant 302726/2023-3 and Grant 407742/2022-0).