14,249 research outputs found
Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
Time Series Classification and Extrinsic Regression are important and
challenging machine learning tasks. Deep learning has revolutionized natural
language processing and computer vision and holds great promise in other fields
such as time series analysis where the relevant features must often be
abstracted from the raw data but are not known a priori. This paper surveys the
current state of the art in the fast-moving field of deep learning for time
series classification and extrinsic regression. We review different network
architectures and training methods used for these tasks and discuss the
challenges and opportunities when applying deep learning to time series data.
We also summarize two critical applications of time series classification and
extrinsic regression, human activity recognition and satellite earth
observation
Use of TanDEM-X and Sentinel Products to Derive Gully Activity Maps in Kunene Region (Namibia) Based on Automatic Iterative Random Forest Approach
Gullies are landforms with specific patterns of shape,
topography, hydrology, vegetation, and soil characteristics. Remote
sensing products (TanDEM-X, Sentinel-1, and Sentinel-2) serve
as inputs into an iterative algorithm, initialized using a micromapping simulation as training data, to map gullies in the northwestern of Namibia. A Random Forest Classifier examines pixels
with similar characteristics in a pool of unlabeled data, and gully
objects are detected where high densities of gully pixels are enclosed
by an alpha shape. Gully objects are used in subsequent iterations
following a mechanism where the algorithm uses the most reliable
pixels as gully training samples. The gully class continuously grows
until an optimal scenario in terms of accuracy is achieved. Results
are benchmarked with manually tagged gullies (initial gully labeled
area <0.3% of the total study area) in two different watersheds
(408 and 302 km2, respectively) yielding total accuracies of >98%,
with 60% in the gully class, Cohen Kappa >0.5, Matthews Correlation Coefficient >0.5, and receiver operating characteristic
Area Under the Curve >0.89. Hence, our method outlines gullies
keeping low false-positive rates while the classification quality has
a good balance for the two classes (gully/no gully). Results show
the most significant gully descriptors as the high temporal radar
signal coherence (22.4%) and the low temporal variability in Normalized Difference Vegetation Index (21.8%). This research builds
on previous studies to face the challenge of identifying and outlining
gully-affected areas with a shortage of training data using global
datasets, which are then transferable to other large (semi-) arid
regions.This research is part of the DEM_HYDR2024 project sup ported by TanDEM-X Science Team, therefore the authors
would like to express thanks to the Deutsches Zentrum für Luft und Raumfahrt (DLR) as the donor for the used TanDEM-X
datasets. They acknowledge the financial support provided by
the Namibia University of Science and Technology (NUST)
within the IRPC research funding programme and to ILMI for
the sponsorship of field trips to identify suitable study areas.
Finally, they would like to express gratitude toward Heidelberg
University and the Kurt-Hiehle-Foundation for facilitating the
suitable work conditions during this research
Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction
Unreliable predictions can occur when using artificial intelligence (AI)
systems with negative consequences for downstream applications, particularly
when employed for decision-making. Conformal prediction provides a
model-agnostic framework for uncertainty quantification that can be applied to
any dataset, irrespective of its distribution, post hoc. In contrast to other
pixel-level uncertainty quantification methods, conformal prediction operates
without requiring access to the underlying model and training dataset,
concurrently offering statistically valid and informative prediction regions,
all while maintaining computational efficiency. In response to the increased
need to report uncertainty alongside point predictions, we bring attention to
the promise of conformal prediction within the domain of Earth Observation (EO)
applications. To accomplish this, we assess the current state of uncertainty
quantification in the EO domain and found that only 20% of the reviewed Google
Earth Engine (GEE) datasets incorporated a degree of uncertainty information,
with unreliable methods prevalent. Next, we introduce modules that seamlessly
integrate into existing GEE predictive modelling workflows and demonstrate the
application of these tools for datasets spanning local to global scales,
including the Dynamic World and Global Ecosystem Dynamics Investigation (GEDI)
datasets. These case studies encompass regression and classification tasks,
featuring both traditional and deep learning-based workflows. Subsequently, we
discuss the opportunities arising from the use of conformal prediction in EO.
We anticipate that the increased availability of easy-to-use implementations of
conformal predictors, such as those provided here, will drive wider adoption of
rigorous uncertainty quantification in EO, thereby enhancing the reliability of
uses such as operational monitoring and decision making
Remote Sensing in Mangroves
The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the worl
A systematic review of the use of Deep Learning in Satellite Imagery for Agriculture
Agricultural research is essential for increasing food production to meet the
requirements of an increasing population in the coming decades. Recently,
satellite technology has been improving rapidly and deep learning has seen much
success in generic computer vision tasks and many application areas which
presents an important opportunity to improve analysis of agricultural land.
Here we present a systematic review of 150 studies to find the current uses of
deep learning on satellite imagery for agricultural research. Although we
identify 5 categories of agricultural monitoring tasks, the majority of the
research interest is in crop segmentation and yield prediction. We found that,
when used, modern deep learning methods consistently outperformed traditional
machine learning across most tasks; the only exception was that Long Short-Term
Memory (LSTM) Recurrent Neural Networks did not consistently outperform Random
Forests (RF) for yield prediction. The reviewed studies have largely adopted
methodologies from generic computer vision, except for one major omission:
benchmark datasets are not utilised to evaluate models across studies, making
it difficult to compare results. Additionally, some studies have specifically
utilised the extra spectral resolution available in satellite imagery, but
other divergent properties of satellite images - such as the hugely different
scales of spatial patterns - are not being taken advantage of in the reviewed
studies.Comment: 25 pages, 2 figures and lots of large tables. Supplementary materials
section included here in main pd
Machine learning to generate soil information
This thesis is concerned with the novel use of machine learning (ML) methods in soil science research. ML adoption in soil science has increased considerably, especially in pedometrics (the use of quantitative methods to study the variation of soils). In parallel, the size of the soil datasets has also increased thanks to projects of global impact that aim to rescue legacy data or new large extent surveys to collect new information. While we have big datasets and global projects, currently, modelling is mostly based on "traditional" ML approaches which do not take full advantage of these large data compilations. This compilation of these global datasets is severely limited by privacy concerns and, currently, no solution has been implemented to facilitate the process. If we consider the performance differences derived from the generality of global models versus the specificity of local models, there is still a debate on which approach is better. Either in global or local DSM, most applications are static. Even with the large soil datasets available to date, there is not enough soil data to perform a fully-empirical, space-time modelling. Considering these knowledge gaps, this thesis aims to introduce advanced ML algorithms and training techniques, specifically deep neural networks, for modelling large datasets at a global scale and provide new soil information. The research presented here has been successful at applying the latest advances in ML to improve upon some of the current approaches for soil modelling with large datasets. It has also created opportunities to utilise information, such as descriptive data, that has been generally disregarded. ML methods have been embraced by the soil community and their adoption is increasing. In the particular case of neural networks, their flexibility in terms of structure and training makes them a good candidate to improve on current soil modelling approaches
Continental-scale land cover mapping at 10 m resolution over Europe (ELC10)
Widely used European land cover maps such as CORINE are produced at medium
spatial resolutions (100 m) and rely on diverse data with complex workflows
requiring significant institutional capacity. We present a high resolution (10
m) land cover map (ELC10) of Europe based on a satellite-driven machine
learning workflow that is annually updatable. A Random Forest classification
model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover
Area frame Survey) dataset. Within the Google Earth Engine cloud computing
environment, the ELC10 map can be generated from approx. 700 TB of Sentinel
imagery within approx. 4 days from a single research user account. The map
achieved an overall accuracy of 90% across 8 land cover classes and could
account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of
the actual value. These accuracies are higher than that of CORINE (100 m) and
other 10-m land cover maps including S2GLC and FROM-GLC10. We found that
atmospheric correction of Sentinel-2 and speckle filtering of Sentinel-1
imagery had minimal effect on enhancing classification accuracy (< 1%).
However, combining optical and radar imagery increased accuracy by 3% compared
to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The conversion of
LUCAS points into homogenous polygons under the Copernicus module increased
accuracy by <1%, revealing that Random Forests are robust against contaminated
training data. Furthermore, the model requires very little training data to
achieve moderate accuracies - the difference between 5K and 50K LUCAS points is
only 3% (86 vs 89%). At 10-m resolution, the ELC10 map can distinguish detailed
landscape features like hedgerows and gardens, and therefore holds potential
for aerial statistics at the city borough level and monitoring property-level
environmental interventions (e.g. tree planting)
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