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pith:NTNFC4YC

pith:2026:NTNFC4YCT274FSOW5JRLRYIGVV

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TERRA-CD: Multi-Temporal Framework for Multi-class and Semantic Change Detection

Omkar Oak, Rujuta Budke, Rukmini Nazre, Suraj Sawant

TERRA-CD supplies 5221 Sentinel-2 image pairs across 232 cities with three annotation layers for land-cover and change detection.

arxiv:2605.14651 v1 · 2026-05-14 · cs.CV

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Claims

C1strongest claim

we present the Temporal Remote-sensing Repository for Analyzing Change Detection (TERRA-CD), a benchmark dataset comprising 5,221 Sentinel-2 image pairs from 2019 and 2024, covering 232 cities across the USA and Europe. The dataset features three distinct annotation schemes: 4-class land cover mapping masks, 3-class vegetation change masks, and 13-class semantic change masks.

C2weakest assumption

The chosen 232 cities and the 2019-2024 time window are assumed to be sufficiently representative for general urban vegetation and semantic change detection tasks; the quality and consistency of the three annotation schemes are also taken as given without detailed validation statistics in the abstract.

C3one line summary

TERRA-CD is a new multi-temporal Sentinel-2 dataset with three levels of change-detection annotations that benchmarks Siamese networks, STANet, Bi-SRNet, and other models for multi-class and semantic change detection.

References

27 extracted · 27 resolved · 0 Pith anchors

[1] Chen, H. & Shi, Z. A spatial-temporal attention-based method and a new dataset for remote sensing image change detection.Remote Sensing.12pp. 1662 (2020) 2020

[2] Adriano, B., Yokoya, N., Xia, J., Miura, H., Liu, W. & Matsuoka, M. Learning from multimodal and multitemporal earth observation data for building damage mapping. ISPRS Journal Of Photogrammetry And R 2021

[3] Daudt, R., Le Saux, B., Boulch, A. & Gousseau, Y. Multitask learning for large- scale semantic change detection.Computer Vision And Image Understanding.187 pp. 102783 (2019) 2019

[4] Yang, K., Xia, G., Liu, Z., Du, B., Yang, W., Pelillo, M. & Zhang, L. Asymmetric siamese networks for semantic change detection in aerial images.IEEE Transactions On Geoscience And Remote Sensing.60pp 2021

[5] Zhu, Q., Guo, X., Li, Z. & Li, D. A review of multi-class change detection for satellite remote sensing imagery.Geo-spatial Information Science.27, 1-15 (2024) 2024

Receipt and verification

First computed	2026-05-17T23:39:03.800821Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

6cda5173029ebfc2c9d6ea62b8e106ad6f63a7fea8f6a79b36d3f6e7bce17be0

Aliases

arxiv: 2605.14651 · arxiv_version: 2605.14651v1 · doi: 10.48550/arxiv.2605.14651 · pith_short_12: NTNFC4YCT274 · pith_short_16: NTNFC4YCT274FSOW · pith_short_8: NTNFC4YC

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/NTNFC4YCT274FSOW5JRLRYIGVV \
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  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 6cda5173029ebfc2c9d6ea62b8e106ad6f63a7fea8f6a79b36d3f6e7bce17be0

Canonical record JSON

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