5,931 research outputs found
Tile2Vec: Unsupervised representation learning for spatially distributed data
Geospatial analysis lacks methods like the word vector representations and
pre-trained networks that significantly boost performance across a wide range
of natural language and computer vision tasks. To fill this gap, we introduce
Tile2Vec, an unsupervised representation learning algorithm that extends the
distributional hypothesis from natural language -- words appearing in similar
contexts tend to have similar meanings -- to spatially distributed data. We
demonstrate empirically that Tile2Vec learns semantically meaningful
representations on three datasets. Our learned representations significantly
improve performance in downstream classification tasks and, similar to word
vectors, visual analogies can be obtained via simple arithmetic in the latent
space.Comment: 8 pages, 4 figures in main text; 9 pages, 11 figures in appendi
Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review
Remote sensing (RS) systems have been collecting
massive volumes of datasets for decades, managing and analyzing
of which are not practical using common software packages and
desktop computing resources. In this regard, Google has developed
a cloud computing platform, called Google Earth Engine (GEE), to
effectively address the challenges of big data analysis. In particular,
this platformfacilitates processing big geo data over large areas and
monitoring the environment for long periods of time. Although this
platformwas launched in 2010 and has proved its high potential for
different applications, it has not been fully investigated and utilized
for RS applications until recent years. Therefore, this study aims
to comprehensively explore different aspects of the GEE platform,
including its datasets, functions, advantages/limitations, and various
applications. For this purpose, 450 journal articles published in
150 journals between January 2010 andMay 2020 were studied. It
was observed that Landsat and Sentinel datasets were extensively
utilized by GEE users. Moreover, supervised machine learning
algorithms, such as Random Forest, were more widely applied to
image classification tasks. GEE has also been employed in a broad
range of applications, such as Land Cover/land Use classification,
hydrology, urban planning, natural disaster, climate analyses, and
image processing. It was generally observed that the number of
GEE publications have significantly increased during the past few
years, and it is expected that GEE will be utilized by more users
from different fields to resolve their big data processing challenges.Peer ReviewedPostprint (published version
Sentinel2GlobalLULC: A Sentinel-2 RGB image tile dataset for global land use/cover mapping with deep learning
Land-Use and Land-Cover (LULC) mapping is relevant for many applications, from Earth system and climate modelling to territorial and urban planning. Global LULC products are continuously developing as remote sensing data and methods grow. However, there still exists low consistency among LULC products due to low accuracy in some regions and LULC types. Here, we introduce Sentinel2GlobalLULC, a Sentinel-2 RGB image dataset, built from the spatial-temporal consensus of up to 15 global LULC maps available in Google Earth Engine. Sentinel2GlobalLULC v2.1 contains 194877 single-class RGB image tiles organized into 29 LULC classes. Each image is a 224 × 224 pixels tile at 10 × 10 m resolution built as a cloud-free composite from Sentinel-2 images acquired between June 2015 and October 2020. Metadata includes a unique LULC annotation per image, together with level of consensus, reverse geo-referencing, global human modification index, and number of dates used in the composite. Sentinel2GlobalLULC is designed for training deep learning models aiming to build precise and robust global or regional LULC maps.This work is part of the project “Thematic Center on Mountain Ecosystem & Remote sensing, Deep learning-AI e-Services University of Granada-Sierra Nevada” (LifeWatch-2019-10-UGR-01), which has been co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program 2014-2020 (POPE), LifeWatch-ERIC action line, within the Workpackages LifeWatch-2019-10-UGR-01 WP-8, LifeWatch-2019-10-UGR-01 WP-7 and LifeWatch-2019-10-UGR-01 WP-4. This work was also supported by projects A-RNM-256-UGR18, A-TIC-458-UGR18, PID2020-119478GB-I00 and P18-FR-4961. E.G. was supported by the European Research Council grant agreement n° 647038 (BIODESERT) and the Generalitat Valenciana, and the European Social Fund (APOSTD/2021/188). We thank the “Programa de Unidades de Excelencia del Plan Propio” of the University of Granada for partially covering the article processing charge
Deep learning detection of types of water-bodies using optical variables and ensembling
Water features are one of the most crucial environmental elements for strengthening climate-change adaptation. Remote sensing (RS) technologies driven by artificial intelligence (AI) have emerged as one of the most sought-after approaches for automating water information extraction and indeed. In this paper, a stacked ensemble model approach is proposed on AquaSat dataset (more than 500,000 images collection via satellite and Google Earth Engine). A one-way Analysis of variance (ANOVA) test and the Kruskal Wallis test are conducted for various optical-based variables at 99% significance level to understand how these vary for different water bodies. An oversampling is done on the training data using Synthetic Minority Oversampling Technique (SMOTE) to solve the problem of class imbalance while the model is tested on an imbalanced data, replicating the real-life situation. To enhance state-of-the-art, the pros of standalone machine learning classifiers and neural networks have been utilized. The stacked model obtained 100% accuracy on the testing data when using the decision tree classifier as the meta model. This study has been cross validated five-fold and will help researchers working in in-situ water bodies detection with the use of stacked model classification
- …