Special Issue "Big Data in Earth Observation: A New Computing Paradigm for Remote Data Analysis"
Deadline for manuscript submissions: 10 August 2021.
Interests: Hyperspectral image analysis; machine (deep) learning; neural networks; multisensor data fusion; high performance computing; cloud computing
Special Issues and Collections in MDPI journals
Interests: hyperspectral remote sensing; deep learning; Graphics Processing Units (GPUs); High Performance Computing (HPC) techniques
Special Issues and Collections in MDPI journals
Special Issue in Remote Sensing: Convolutional Neural Networks for Object Detection
Special Issue in Remote Sensing: Artificial Intelligence Algorithm for Remote Sensing Imagery Processing
Interests: Hyperspectral image processing, Remote Sensing Big Data Processing, Parallel Computing, Machine Learning, Cloud Computing
Special Issues and Collections in MDPI journals
With the recent advances made in the Earth Observation (EO) field, the use of remote sensing information captured by available sensors (located on aerial and/or satellite platforms) has acquired a very important role in a wide range of human activities such as the management of environment and natural resources (including forests, water, geological and mineralogical resources), prevention of risks and catastrophes, planning of urban and rural spaces, detection of military objectives and intelligence tasks, among others. This has been fostered by the fact that a detailed characterization of the Earth's surface is now possible using the data collected by current remote sensing instruments for EO, which are able to collect data with higher spatial and spectral resolutions, thus allowing for the acquisition of a large variety of remotely sensed images, from panchromatic and RGB data to multispectral and hyperspectral scenes, from LiDAR and radar sensors, to thermal and optical images, and from low to medium, high and very high spatial resolutions.
For instance, the sensors capable of acquiring images with hundreds of spectral bands (called imaging spectrometers) are able to gather large amounts of information for the same area by recording hundreds of measurements in the spectral domain at different wavelengths. This allows "to see what the human eye cannot," making possible the generation of "data cubes," also known as hyperspectral images (HSI) with very large dimensionality. These images permit a very precise characterization of the terrestrial surface. For example, NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) sensor is able to capture HSI scenes with 224 spectral bands between 0.4 and 2.5 micrometers, and spatial resolution of about 20 meters per pixel. Such wealth of spatial and spectral information (despite imposing important computational requirements) has opened new possibilities in many applications, including the detailed characterization of agricultural and urban areas, or the monitoring and prevention of natural disasters such as forest fires, oil spills and other types of chemical pollution.
This Special Issue on “Big Data in Earth Observation: a new computing paradigm for remote data analysis" is intended to introduce the latest techniques in high performance computing (HPC) to the development and application of new image processing techniques for an adequate and computationally efficient exploitation of remotely sensed scenes from a Big Data point of view, exploring new computationally efficient models for extracting information from huge remote sensing datasets, with particular interest in the development of parallel and distributed techniques based on graphical processing units (GPUs) and grid/cloud computing platforms.
The goal of this Special Issue is to collect the latest and most advanced ideas regarding the new and efficient techniques for extracting information based on the new trends in advanced learning algorithms (including the newest machine and deep learning approaches).
Dr. Juan M. Haut
Ms. Mercedes E. Paoletti
Dr. Zebin Wu
Manuscript Submission Information
Manuscripts should be submitted online at www.dlhwdz.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Big data
- Neural Networks
- Deep learning
- Cloud computing
- Heterogeneous computing
- Remote sensing
- Image processing
- Machine learning
- High performance computing
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
(1) Combining Distributed Spatial Computation and a Parallel Evolutionary Algorithm for Fast Urban LiDAR Flight Path Planning
Anh Vu Vo1, Debra F. Laefer2, Jonathan Byrne3
The use of parallel and distributed computing in the field of Earth bservation is well recognised in the context of post-acquisition data analysis and processing. Namely, coupling a large number of computing processors to work concurrently can accelerate computations involving large amounts of data aggregated from decades of sensing activities. In this paper, a different perspective is taken. We investigate the use of parallel and distributed computing for planning data acquisition. Specifically, a computing framework is introduced to facilitate LiDAR flight path planning for dense urban environments.
As the majority of standard practices in airborne LiDAR data acquisition were developed for topographic mapping, mapping dense urban environments requires re-evaluation. For instance, flight line directions must be planned to alleviate occlusions caused by tall features and to maximise data capture on building facades. Such problems do not arise typically in topographic mapping, but they are crucial for comprehensive mapping of dense urban environments.
The flight path planning in this research is formulated as a geometric problem optimised with an evolutionary algorithm, which is capable of optimising open-ended problems that have many possible solutions. The evolutionary optimisation operates by iteratively evaluating a set of candidate flight paths and combining the best performing candidates, which are in part randomly mutated to expand the search space. Evaluating the performance of a large number of flight path candidates repeatedly is a time consuming process. The speed of the process decides the feasibility of the optimisation strategy.
In this research, two levels of parallelisation are combined to curb the required computational time of the LiDAR flight path optimisation. At the first level, multiple flight path candidates of the same generation are evaluated simultaneously. This level of parallelisation is straightforward since evolutionary algorithms are inherently parallelisable. At the second level, the evaluation of each flight path candidate is conducted using a novel distributed algorithm. The algorithm, which is based on the Map Reduce programming model, makes use of multiple computing cores of a shared-nothing cluster to reduce the runtime of each evaluation. The second level of parallelisation is crucial to the reduction of the overall runtime given that the evaluation of each flight scenario requires a complex, intensive computation that involves a large amount of spatial data.
The flight path optimisation strategy is demonstrated through the planning of an actual extremely highresolution LiDAR scanning over an area of 1km2 of the Sunset Park area in Brooklyn, New York. In that project, each flight path evaluation took under 3 minutes, bringing the total time for achieving a converged result to approximately 30 hours. To confirm the validity of the optimisation, the best- and worst-performing flight path candidates were flown in May 2019. The data acquired from the flight provide robust evidence to justify the optimisation strategy and provide important insight into the LiDAR flight planning process for dense urban environments.
(2) PyCircularStats: A Python-based tool for circular statistics and graphical analysis
Aurora Cuartero Sáez, Pablo García Rodríguez
Abstract: Circular data, as part of directional data, is used in a wide range of fields, such as Geology, Biology, Meteorology, and Geomatics. It differs from traditional linear data because it is closed and has no beginning or end along the actual line, i.e. circular data occurs around a circle, normally measured in degrees. Analyzing directional data, in particular circular data, requires methods that are available in libraries with a well-known prestige as Python including its SciPy, NumPy or SciKit-Learn libs. However, these libraries have a specific area of expertise and do not combine information in a useful way for two-dimensional data analysis. In this paper, an open-source library has been implemented to be executed by the Python interpreter, called PyCircularStats. To demonstrate the potential of PyCircularStats and to show some of its features, the integration of the proposed circular statistics is described with the objective of analyzing vector data. As an example, a particular case is shown to analyze the positional accuracy of satellite image of LandSat-8 in Caceres, Spain, with this graphical circular statistic. Additionally, this paper performs a comparison between PyCircularStats and VecStatGraphs2D, another vector analysis using graphical and analytical methods developed in R, and also the improvements and advantages developed in a new PycircularStats tool based on Python have also been presented.