Evaluation of Sentinel-2 Data for Automatic Maasai Boma Mapping

Cheng, Keli; Bajkowski, Trevor M.; Scott, Grant

doi:10.1109/aipr52630.2021.9762131

Cited by 1 publication

(3 citation statements)

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“…DeepSN provides a more noticeable improvement with the same resolution data. In the absence of DeepSN, the ProxylessNAS achieves an F1 score of 73.42% when utilizing annual median data from Sentinel-2 [6]. Nevertheless, when incorporating multi-temporal Sentinel-2 data in conjunction with DeepSN, the F1 score experiences a substantial improvement, reaching 92.12%.…”

Section: Performance Of Deep Seasonal Networkmentioning

confidence: 99%

“…The comparison reveals that DeepSN not only maximizes the utilization of Sentinel-2 data but also attains an equivalent level of performance on the Boma classification task as using VHR images. The results using ProxylessNAS are copied from [5,6].…”

Section: Performance Of Deep Seasonal Networkmentioning

confidence: 99%

“…Additionally, access to VHR typically has a cost that may be prohibitive for non-profit entities. In [6], multi-spectral medium-resolution Sentinel-2 data were evaluated as a possible alternative for Google Maps VHR imagery. However, the performance gap of models using these two types of data remains wide, despite the enhanced spectral fidelity and diversity of data bands available from Sentinel-2 data.…”

Section: Introductionmentioning

confidence: 99%

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Deep Seasonal Network for Remote Sensing Imagery Classification of Multi-Temporal Sentinel-2 Data

Cheng,

Scott

2023

Remote Sensing

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As a medium-resolution multi-temporal data source, Sentinel-2 data has the potential to match the performance of using very-high-resolution (VHR) images in deep learning applications. To fully leverage the multi-temporal nature of Sentinel-2 data, we introduce the Deep Seasonal Network (DeepSN). This composite architecture combines a pre-trained deep convolutional neural network (DCNN) for visual feature extraction with a long short-term memory (LSTM) model to capture temporal information and make classification predictions. We evaluate the effectiveness of DeepSN on a Maasai Boma classification task in the Tanzania region. The DeepSN takes a sequence of four seasonal data, each spanning three months, for Boma prediction. Through cross-season validation experiments, we compare various advanced DCNNs and select EfficientNet as the backbone for DeepSN, as it performs the best. DeepSN with an EfficientNet backbone achieves a significant 19% improvement in the F1 score compared to plain EfficientNet for the Boma classification task. This work introduces a versatile composite architecture capable of handling multi-temporal data efficiently, providing flexibility in choosing the most suitable feature extraction backbone. The performance of DeepSN demonstrates the viability of utilizing medium-resolution multi-temporal data instead of high-resolution images for diverse tasks.

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Section: Performance Of Deep Seasonal Networkmentioning

confidence: 99%

Section: Performance Of Deep Seasonal Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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