2020
DOI: 10.1016/j.dib.2020.106553
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LEM+ dataset: For agricultural remote sensing applications

Abstract: Remote sensing allows obtaining information on agriculture regularly with non-invasive measurement approaches. Field data is crucial for adequate agricultural monitoring by remote sensing. However, public available field data are scarce, mainly in tropical regions, where agriculture is highly dynamic. The present publication aims to support the reduction of this gap. The LEM+ dataset provides information monthly about 16 land use classes for 1854 fields from October 2019 to September 2020 (one Brazilian agricu… Show more

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Cited by 14 publications
(6 citation statements)
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“…The reference data set (ref_sf), provided by Oldoni et al (2020), was collected in two fieldwork campaigns in March and August 2020. Oldoni et al (2020) draw the field boundaries in-situ on top of images Sentinel-2, with a spatial resolution of 10 meters. segmetric includes only a portion of this data set.…”
Section: Package Segmetric In Actionmentioning
confidence: 99%
“…The reference data set (ref_sf), provided by Oldoni et al (2020), was collected in two fieldwork campaigns in March and August 2020. Oldoni et al (2020) draw the field boundaries in-situ on top of images Sentinel-2, with a spatial resolution of 10 meters. segmetric includes only a portion of this data set.…”
Section: Package Segmetric In Actionmentioning
confidence: 99%
“…The sharing of methods, data, and findings is essential in the current trend of big data on remote sensing [64]. Although acquired two years ago, the database built and shared by Oldoni et al [65] with some coincident areas was useful for better understanding of our study area, since it was not possible to go through it entirely.…”
Section: Lulc Mapping Challenges and Variables Importancementioning
confidence: 99%
“…As a result, it was observed that the accuracy was better when using DL techniques (specifically, CNN), rather than the other considered methods-indeed, the CNN-based approach showed a more stable behavior. The research presented in [33,34] apply DL techniques to classify not only Campo Verde but also Luis Eduardo database [45] (another agriculture database from Brazil). Both works use approaches based on an FCN and similar parameters including a 32 × 32 patch size and assess the methodology over individual images and sequences composed by several images.…”
Section: Related Workmentioning
confidence: 99%