Cropland maps are a core and critical component of remote-sensing-based
agricultural monitoring, providing dense and up-to-date information about
agricultural development. Machine learning is an effective tool for large-scale
agricultural mapping, but relies on geo-referenced ground-truth data for model
training and testing, which can be scarce or time-consuming to obtain. In this
study, we explore the usefulness of combining a global cropland dataset and a
hand-labeled dataset to train machine learning models for generating a new
cropland map for Nigeria in 2020 at 10 m resolution. We provide the models with
pixel-wise time series input data from remote sensing sources such as
Sentinel-1 and 2, ERA5 climate data, and DEM data, in addition to binary labels
indicating cropland presence. We manually labeled 1827 evenly distributed
pixels across Nigeria, splitting them into 50\% training, 25\% validation, and
25\% test sets used to fit the models and test our output map. We evaluate and
compare the performance of single- and multi-headed Long Short-Term Memory
(LSTM) neural network classifiers, a Random Forest classifier, and three
existing 10 m resolution global land cover maps (Google's Dynamic World, ESRI's
Land Cover, and ESA's WorldCover) on our proposed test set. Given the regional
variations in cropland appearance, we additionally experimented with excluding
or sub-setting the global crowd-sourced Geowiki cropland dataset, to
empirically assess the trade-off between data quantity and data quality in
terms of the similarity to the target data distribution of Nigeria. We find
that the existing WorldCover map performs the best with an F1-score of 0.825
and accuracy of 0.870 on the test set, followed by a single-headed LSTM model
trained with our hand-labeled training samples and the Geowiki data points in
Nigeria, with a F1-score of 0.814 and accuracy of 0.842.

利用机器学习技术结合全球农田数据集和手工标注数据，对尼日利亚进行了 10 米分辨率的新农田图生成，通过评估和比较不同模型及全球土地覆盖地图的性能，发现现有的 WorldCover 地图在测试集上表现最佳。

在数据稀缺环境下使用深度学习进行大规模农田绘制：尼日利亚案例研究

Country-Scale Cropland Mapping in Data-Scarce Settings Using Deep  Learning: A Case Study of Nigeria

Land cover maps are a pivotal element in a wide range of Earth Observation
(EO) applications. However, annotating large datasets to develop supervised
systems for remote sensing (RS) semantic segmentation is costly and
time-consuming. Unsupervised Domain Adaption (UDA) could tackle these issues by
adapting a model trained on a source domain, where labels are available, to a
target domain, without annotations. UDA, while gaining importance in computer
vision, is still under-investigated in RS. Thus, we propose a new lightweight
model, GeoMultiTaskNet, based on two contributions: a GeoMultiTask module
(GeoMT), which utilizes geographical coordinates to align the source and target
domains, and a Dynamic Class Sampling (DCS) strategy, to adapt the semantic
segmentation loss to the frequency of classes. This approach is the first to
use geographical metadata for UDA in semantic segmentation. It reaches
state-of-the-art performances (47,22% mIoU), reducing at the same time the
number of parameters (33M), on a subset of the FLAIR dataset, a recently
proposed dataset properly shaped for RS UDA, used for the first time ever for
research scopes here.

提出了一种新的轻量级模型 ——GeoMultiTaskNet，使用地理坐标对源领域和目标领域进行信息对齐，采用动态类别抽样（Dynamic Class Sampling）策略来适应语义分割的损失函数，将其用于自然资源遥感图像分割，并取得了最先进的性能。

GeoMultiTaskNet: 利用地理坐标进行遥感非监督域自适应

GeoMultiTaskNet: remote sensing unsupervised domain adaptation using  geographical coordinates

New remote sensing sensors now acquire high spatial and spectral Satellite
Image Time Series (SITS) of the world. These series of images are a key
component of classification systems that aim at obtaining up-to-date and
accurate land cover maps of the Earth's surfaces. More specifically, the
combination of the temporal, spectral and spatial resolutions of new SITS makes
possible to monitor vegetation dynamics. Although traditional classification
algorithms, such as Random Forest (RF), have been successfully applied for SITS
classification, these algorithms do not make the most of the temporal domain.
Conversely, some approaches that take into account the temporal dimension have
recently been tested, especially Recurrent Neural Networks (RNNs). This paper
proposes an exhaustive study of another deep learning approaches, namely
Temporal Convolutional Neural Networks (TempCNNs) where convolutions are
applied in the temporal dimension. The goal is to quantitatively and
qualitatively evaluate the contribution of TempCNNs for SITS classification.
This paper proposes a set of experiments performed on one million time series
extracted from 46 Formosat-2 images. The experimental results show that
TempCNNs are more accurate than RF and RNNs, that are the current state of the
art for SITS classification. We also highlight some differences with results
obtained in computer vision, e.g. about pooling layers. Moreover, we provide
some general guidelines on the network architecture, common regularization
mechanisms, and hyper-parameter values such as batch size. Finally, we assess
the visual quality of the land cover maps produced by TempCNNs.

本文提出 Temporal Convolutional Neural Networks (TempCNNs) 来实现 Satellite Image Time Series (SITS) 的分类，通过在时间维度上应用卷积来完成。作者进行了一系列实验，证明了 TempCNNs 比传统算法（如 Random Forest）和近期广泛应用的 Recurrent Neural Networks（RNNs）更加准确，提供了网络结构、正则化机制和超参数等方面的指导，并确认了 TempCNNs 在土地覆盖地图上的可视化效果。