From Local to Global: A Transfer Learning-Based Approach for Mapping Poplar Plantations at Large Scale

Hamrouni, Yousra; Paillassa, Eric; Chéret, Véronique; Monteil, Claude

doi:10.1109/m2garss47143.2020.9105218

Cited by 1 publication

(1 citation statement)

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“…Training samples collection is a critical problem which scales up as scale becomes larger (e.g., multiple S2 tiles or Country level) [10] and time series longer. Furthermore, supervised machine learning methods work effectively when applied on a user-defined study area, but they tend to fail (despite transfer learning and domain adaptation paradigms) when applied across large space and time scales [11]- [14]. This happens because (1) the target classes are highly variable and embedded in a heterogeneous and complex natural or anthropogenic landscape, and (2) insufficient training samples are available to adequately represent the high and fast spatiotemporal variability.…”

Section: Introductionmentioning

confidence: 99%

Automatic Large-Scale Precise Mapping and Monitoring of Agricultural Fields at Country Level With Sentinel-2 SITS

Solano‐Correa

Meshkini

Bovolo

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Availability of multi-temporal images, such as the Sentinel-2 (S2) ones, offers accurate spatial, spectral and temporal information to effectively monitor vegetation, more specifically agriculture. Agricultural practices can benefit from temporally dense Satellite Image Time Series (SITS) for accurate understanding of the phenological evolution and behavior of crops. Developing techniques that deal with high spatial correlation and high temporal resolution requires a shift in the processing paradigm and poses new challenges in terms of data processing and methodology. This paper presents an automatic approach to large-scale precise mapping of small agricultural fields based on the analysis of S2-SITS at Country level. The approach deals with a flexible and automatic processing chain for massive data and was tested at Country level. The large-scale application requires to consider: (i) the management of big amount of data with particular attention to download and preprocessing of S2-SITS; and (ii) multi-temporal fine characterization of crop fields accounting for the strong variability in size and phenological behaviors when mapping at large scale. Both challenges are addressed in an automatic way by exploiting and/or updating state-of-the-art methodologies. Promising results have been obtained and validated over 2017 and 2018 agrarian years for Italy.

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