RapidAI4EO: A Corpus for Higher Spatial and Temporal Reasoning

Marchisio, Giovanni; Helber, Patrick; Bischke, Benjamin; Davis, Timothy; Şenaras, Çağlar; Zanaga, Daniele; Kerchove, Ruben Van De; Wania, Annett

doi:10.1109/igarss47720.2021.9553080

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…RapidAI4EO [2] will establish the basis for the next-generation Copernicus Land Monitoring Service (CLMS) under the sponsorship of the European Union's Horizon 2020 program. The idea of such a large corpus comes from patch-based LULC EuroSAT [6] and BigEarthNet [7] corpora.…”

Section: Datasetmentioning

confidence: 99%

“…The data from such satellites were used by researchers to analyze and track land use and land changes over time in response to human factors such as climate change, urbanization, deforestation, wildfires, etc. RapidAI4EO [2], a new satellite imagery corpus, aims to provide better monitoring of Land Use (LU), Land Cover (LC), and LULC change at a much higher level of detail and temporal frequency than is currently possible [2]. The objective of this paper is to explore the advantages of using multi-temporal (monthly) over monotemporal (single time-step) satellite images.…”

Section: Introductionmentioning

confidence: 99%

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Rapidai4Eo: Mono-and Multi-Temporal Deep Learning Models for Updating the Corine land Cover Product

Bhugra¹,

Bischke²,

Werner³

et al. 2022

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

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In the remote sensing community, Land Use Land Cover (LULC) classification with satellite imagery is a main focus of current research activities. Accurate and appropriate LULC classification, however, continues to be a challenging task. In this paper, we evaluate the performance of multitemporal (monthly time series) compared to mono-temporal (single time step) satellite images for multi-label classification using supervised learning on the RapidAI4EO dataset. As a first step, we trained our CNN model on images at a single time step for multi-label classification, i.e. monotemporal. We incorporated time-series images using a LSTM model to assess whether or not multi-temporal signals from satellites improves CLC classification. The results demonstrate an improvement of approximately 0.89% in classifying satellite imagery on 15 classes using a multi-temporal approach on monthly time series images compared to the monotemporal approach. Using features from multi-temporal or mono-temporal images, this work is a step towards an efficient change detection and land monitoring approach.

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Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapidai4Eo: Mono-and Multi-Temporal Deep Learning Models for Updating the Corine land Cover Product

Bhugra¹,

Bischke²,

Werner³

et al. 2022

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

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“…In this study, we adapt one of the state-of-the-art SSL architectures, called SimSiam (Chen, 2021), to pre-train our attention-based crop classifier. Since we need a large number of unlabeled samples to train our model in a self-supervised manner effectively, we combine the advantages of two datasets: RapidAI4EO (Marchisio, 2021) and EuroCrops. EuroCrops provides the boundary of the fields around Europe.…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Learning for Crop Classification Using Planet Fusion

Senaras¹,

Holden

Davis

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Benefiting from the high cadence and spatial resolution of the new generation of Earth observation satellites, remote sensing technology is allowing us to derive more valuable information for the agricultural sector. Crop classification is one of the fundamental information derivatives from Earth observation data researchers used for food security, crop monitoring, and economic assessment. The robustness of a crop classification model to variations in environmental and management conditions due to time and location is one of the crucial requirements. To achieve this, we developed a novel self-supervised method using the advantage of unlabeled samples and transformer architectures. We used six different areas in Germany and four years to evaluate the robustness of the model. Our experiments showed that self-supervised deep learning methods could provide a significant advantage in handling these variations. In some cases, we observed around 30 percentage points improvements in F1-score performance compared to a Random Forest based model.

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Major TOM: Expandable Datasets for Earth Observation

Francis,

Czerkawski

2024

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium

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RapidAI4EO: A Corpus for Higher Spatial and Temporal Reasoning

Cited by 4 publications

References 7 publications

Rapidai4Eo: Mono-and Multi-Temporal Deep Learning Models for Updating the Corine land Cover Product

Rapidai4Eo: Mono-and Multi-Temporal Deep Learning Models for Updating the Corine land Cover Product

Self-Supervised Learning for Crop Classification Using Planet Fusion

Major TOM: Expandable Datasets for Earth Observation

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