Distributed processing of large remote sensing images using MapReduce - A case of Edge Detection

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Advances in sensor technology and their ever increasing repositories of the collected data are revolutionizing the mechanisms remotely sensed data are collected, stored and processed. This exponential growth of data archives and the increasing user’s demand for real-and near-real time remote sensing data products has pressurized remote sensing service providers to deliver the required services. The remote sensing community has recognized the challenge in processing large and complex satellite datasets to derive customized products. To address this high demand in computational resources, several efforts have been made in the past few years towards incorporation of high-performance computing models in remote sensing data collection, management and analysis. This study adds an impetus to these efforts by introducing the recent advancements in distributed computing technologies, MapReduce programming paradigm, to the area of remote sensing. The MapReduce model which is developed by Google Inc. encapsulates the efforts of distributed computing in a highly simplified single library. This simple but powerful programming model can provide us distributed environment without having deep knowledge of parallel programming. This thesis presents a MapReduce based processing of large satellite images a use case scenario of edge detection methods. Deriving from the conceptual massive remote sensing image processing applications, a prototype of edge detection methods was implemented on MapReduce framework using its open-source implementation, the Apache Hadoop environment. The experiences of the implementation of the MapReduce model of Sobel, Laplacian, and Canny edge detection methods are presented. This thesis also presents the results of the evaluation the effect of parallelization using MapReduce on the quality of the output and the execution time performance tests conducted based on various performance metrics. The MapReduce algorithms were executed on a test environment on heterogeneous cluster that supports the Apache Hadoop open-source software. The successful implementation of the MapReduce algorithms on a distributed environment demonstrates that MapReduce has a great potential for scaling large-scale remotely sensed images processing and perform more complex geospatial problems

A case study of advancing remote sensing image analysis

Big data and cloud computing are two phenomena, which have gained significant reputation over the last few years. In computer science the approach shifted towards distributed architectures and high performance computing. In case of geographical information systems (GIS) and remote sensing image analysis, the new paradigms have already been successfully applied to several problems, and systems have been developed to support processing of geographical and remote sensing data in the cloud. However, due to different circumstances many previous workflows have to be reconsidered and redesigned. Our goal is to show a way how the existing approaches to remote sensing image analysis can be advanced to take advantages of these new paradigms. The task aiming in shifting the algorithms shall require a moderate effort and must avoid the complete redesign and reimplementation of the existing approaches. We present the whole journey as a case study using an existing industrial workflow for demonstration. Nevertheless, we define the rules of thumb, which can come in hand when shifting any existing GIS workflows. Our case study is the workflow of waterlogging and flood detection, which is an operative task at the Institute of Geodesy, Cartography and Remote Sensing (FÖMI). This task in currently operational using a semi-automatic single machine approach involving multiple software. The workflow is neither efficient nor scalable, thus it is not applicable in emergency situations where quick response is required. We present an approach utilizing distributed computing, which enables the automated execution of this task on large input data with much better response time. The approach is based on the well-known MapReduce paradigm, its open-source implementation, the Apache Hadoop framework and the AEGIS geospatial toolkit. This enables the replacement of multiple software to a single, generic framework. Results show that significant performance benefits can be achieved at the expense of minor accuracy loss

Big Geospatial Data processing in the IQmulus Cloud

Remote sensing instruments are continuously evolving in terms of spatial, spectral and temporal resolutions and hence provide exponentially increasing amounts of raw data. These volumes increase significantly faster than computing speeds. All these techniques record lots of data, yet in different data models and representations; therefore, resulting datasets require harmonization and integration prior to deriving meaningful information from them. All in all, huge datasets are available but raw data is almost of no value if not processed, semantically enriched and quality checked. The derived information need to be transferred and published to all level of possible users (from decision makers to citizens). Up to now, there are only limited automatic procedures for this; thus, a wealth of information is latent in many datasets. This paper presents the first achievements of the IQmulus EU FP7 research and development project with respect to processing and analysis of big geospatial data in the context of flood and waterlogging detection

Development of a New Framework for Distributed Processing of Geospatial Big Data

Geospatial technology is still facing a lack of “out of the box” distributed processing solutions which are suitable for the amount and heterogeneity of geodata, and particularly for use cases requiring a rapid response. Moreover, most of the current distributed computing frameworks have important limitations hindering the transparent and flexible control of processing (and/or storage) nodes and control of distribution of data chunks. We investigated the design of distributed processing systems and existing solutions related to Geospatial Big Data. This research area is highly dynamic in terms of new developments and the re-use of existing solutions (that is, the re-use of certain modules to implement further specific developments), with new implementations continuously emerging in areas such as disaster management, environmental monitoring and earth observation. The distributed processing of raster data sets is the focus of this paper, as we believe that the problem of raster data partitioning is far from trivial: a number of tiling and stitching requirements need to be addressed to be able to fulfil the needs of efficient image processing beyond pixel level. We attempt to compare the terms Big Data, Geospatial Big Data and the traditional Geospatial Data in order to clarify the typical differences, to compare them in terms of storage and processing backgrounds for different data representations and to categorize the common processing systems from the aspect of distributed raster processing. This clarification is necessary due to the fact that they behave differently on the processing side, and particular processing solutions need to be developed according to their characteristics. Furthermore, we compare parallel and distributed computing, taking into account the fact that these are used improperly in several cases. We also briefly assess the widely-known MapReduce paradigm in the context of geospatial applications. The second half of the article reports on a new processing framework initiative, currently at the concept and early development stages, which aims to be capable of processing raster, vector and point cloud data in a distributed IT ecosystem. The developed system is modular, has no limitations on programming language environment, and can execute scripts written in any development language (e.g. Python, R or C#)

Big Data Computing for Geospatial Applications

The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms

Performance-Aware High-Performance Computing for Remote Sensing Big Data Analytics

The incredible increase in the volume of data emerging along with recent technological developments has made the analysis processes which use traditional approaches more difficult for many organizations. Especially applications involving subjects that require timely processing and big data such as satellite imagery, sensor data, bank operations, web servers, and social networks require efficient mechanisms for collecting, storing, processing, and analyzing these data. At this point, big data analytics, which contains data mining, machine learning, statistics, and similar techniques, comes to the help of organizations for end-to-end managing of the data. In this chapter, we introduce a novel high-performance computing system on the geo-distributed private cloud for remote sensing applications, which takes advantages of network topology, exploits utilization and workloads of CPU, storage, and memory resources in a distributed fashion, and optimizes resource allocation for realizing big data analytics efficiently

Sideloading - Ingestion Of large point clouds into the apache spark big data engine

In the geospatial domain we have now reached the point where data volumes we handle have clearly grown beyond the capacity of most desktop computers. This is particularly true in the area of point cloud processing. It is therefore naturally lucrative to explore established big data frameworks for big geospatial data. The very first hurdle is the import of geospatial data into big data frameworks, commonly referred to as data ingestion. Geospatial data is typically encoded in specialised binary file formats, which are not naturally supported by the existing big data frameworks. Instead such file formats are supported by software libraries that are restricted to single CPU execution. We present an approach that allows the use of existing point cloud file format libraries on the Apache Spark big data framework. We demonstrate the ingestion of large volumes of point cloud data into a compute cluster. The approach uses a map function to distribute the data ingestion across the nodes of a cluster. We test the capabilities of the proposed method to load billions of points into a commodity hardware compute cluster and we discuss the implications on scalability and performance. The performance is benchmarked against an existing native Apache Spark data import implementation