10 research outputs found
A flexible multi-temporal and multi-modal framework for Sentinel-1 and Sentinel-2 analysis ready data
The rich, complementary data provided by Sentinel-1 and Sentinel-2 satellite constellations host considerable potential to transform Earth observation (EO) applications. However, a substantial amount of effort and infrastructure is still required for the generation of analysis-ready data (ARD) from the low-level products provided by the European Space Agency (ESA). Here, a flexible Python framework able to generate a range of consistent ARD aligned with the ESA-recommended processing pipeline is detailed. Sentinel-1 Synthetic Aperture Radar (SAR) data are radiometrically calibrated, speckle-filtered and terrain-corrected, and Sentinel-2 multi-spectral data resampled in order to harmonise the spatial resolution between the two streams and to allow stacking with multiple scene classification masks. The global coverage and flexibility of the framework allows users to define a specific region of interest (ROI) and time window to create geo-referenced Sentinel-1 and Sentinel-2 images, or a combination of both with closest temporal alignment. The framework can be applied to any location and is user-centric and versatile in generating multi-modal and multi-temporal ARD. Finally, the framework handles automatically the inherent challenges in processing Sentinel data, such as boundary regions with missing values within Sentinel-1 and the filtering of Sentinel-2 scenes based on ROI cloud coverage
Earth system data cubes unravel global multivariate dynamics
Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model-data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model-data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved
Earth system data cubes unravel global multivariate dynamics
Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model-data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model-data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved
Earth system data cubes unravel global multivariate dynamics
Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model- data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model-data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model-data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries
Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture
Big streams of Earth images from satellites or other platforms (e.g., drones
and mobile phones) are becoming increasingly available at low or no cost and
with enhanced spatial and temporal resolution. This thesis recognizes the
unprecedented opportunities offered by the high quality and open access Earth
observation data of our times and introduces novel machine learning and big
data methods to properly exploit them towards developing applications for
sustainable and resilient agriculture. The thesis addresses three distinct
thematic areas, i.e., the monitoring of the Common Agricultural Policy (CAP),
the monitoring of food security and applications for smart and resilient
agriculture. The methodological innovations of the developments related to the
three thematic areas address the following issues: i) the processing of big
Earth Observation (EO) data, ii) the scarcity of annotated data for machine
learning model training and iii) the gap between machine learning outputs and
actionable advice.
This thesis demonstrated how big data technologies such as data cubes,
distributed learning, linked open data and semantic enrichment can be used to
exploit the data deluge and extract knowledge to address real user needs.
Furthermore, this thesis argues for the importance of semi-supervised and
unsupervised machine learning models that circumvent the ever-present challenge
of scarce annotations and thus allow for model generalization in space and
time. Specifically, it is shown how merely few ground truth data are needed to
generate high quality crop type maps and crop phenology estimations. Finally,
this thesis argues there is considerable distance in value between model
inferences and decision making in real-world scenarios and thereby showcases
the power of causal and interpretable machine learning in bridging this gap.Comment: Phd thesi
Towards Causal Inference for Spatio-Temporal Data: Conflict and Forest Loss in Colombia
In many data scientific problems, we are interested not only in modeling the
behaviour of a system that is passively observed, but also in inferring how the
system reacts to changes in the data generating mechanism. Given knowledge of
the underlying causal structure, such behaviour can be estimated from purely
observational data. To do so, one typically assumes that the causal structure
of the data generating mechanism can be fully specified. Furthermore, many
methods assume that data are generated as independent replications from that
mechanism. Both of these assumptions are usually hard to justify in practice:
datasets often have complex dependence structures, as is the case for
spatio-temporal data, and the full causal structure between all involved
variables is hardly known. Here, we present causal models that are adapted to
the characteristics of spatio-temporal data, and which allow us to define and
quantify causal effects despite incomplete causal background knowledge. We
further introduce a simple approach for estimating causal effects, and a
non-parametric hypothesis test for these effects being zero. The proposed
methods do not rely on any distributional assumptions on the data, and allow
for arbitrarily many latent confounders, given that these confounders do not
vary across time (or, alternatively, they do not vary across space). Our
theoretical findings are supported by simulations and code is available online.
This work has been motivated by the following real-world question: how has the
Colombian conflict influenced tropical forest loss? There is evidence for both
enhancing and reducing impacts, but most literature analyzing this problem is
not using formal causal methodology. When applying our method to data from 2000
to 2018, we find a reducing but insignificant causal effect of conflict on
forest loss. Regionally, both enhancing and reducing effects can be identified.Comment: 29 pages, 8 figure
On-Demand Processing of Data Cubes from Satellite Image Collections with the gdalcubes Library
Earth observation data cubes are increasingly used as a data structure to make large collections of satellite images easily accessible to scientists. They hide complexities in the data such that data users can concentrate on the analysis rather than on data management. However, the construction of data cubes is not trivial and involves decisions that must be taken with regard to any particular analyses. This paper proposes on-demand data cubes, which are constructed on the fly when data users process the data. We introduce the open-source C++ library and R package gdalcubes for the construction and processing of on-demand data cubes from satellite image collections, and show how it supports interactive method development workflows where data users can initially try methods on small subsamples before running analyses on high resolution and/or large areas. Two study cases, one on processing Sentinel-2 time series and the other on combining vegetation, land surface temperature, and precipitation data, demonstrate and evaluate this implementation. While results suggest that on-demand data cubes implemented in gdalcubes support interactivity and allow for combining multiple data products, the speed-up effect also strongly depends on how original data products are organized. The potential for cloud deployment is discussed