InterCloud is an open-source, distributed framework for automatic interpretation of remote sensing and medical image data built on top of Hadoop. This new system can be understood as a functional and architectural redesign of the first version of InterIMAGE. InterCloud delivers some important features for automatically interpreting big data using expert knowledge. It:
- Enables the user to embed expert knowledge into the system intuitively through the definition of semantic networks and rule sets defined in a high-level programming language;
- Allows programmers to extend the system by adding their own algorithms straight forwardly;
- Delivers the robustness of MapReduce for cluster computing without the complexity of handling the Hadoop programming language.
InterCloud and its source code is available upon request.
ICP and its package tools are noncommercial. You may not use this work for commercial purposes. For any reuse or distribution, you must make clear to others the license terms of this work. Any of these conditions can be waived if you get written permission from LVC/PUC-Rio.
If you use the InterCloud in any of your experiments or researches that lead to a scientific publication, please cite the one or more of the following papers:
InterCloud Framework: Ferreira, R.S.; Bentes, C.; Costa, G.A.O.P.; Oliveira, D.A.B.; Happ, P.N.; Feitosa, R.Q; Gamba, P. A Set of Methods to Support Object-Based Distributed Analysis of Large Volumes of Earth Observation Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 10, p. 681-690, 2017.
Segmentation: Happ, P.N.; Costa, G.A.O.P.; Bentes, C.; Feitosa, R.Q; Ferreira, R.S.; Farias, R. A Cloud Computing Strategy for Region-Growing Segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 9, p. 5294-5303, 2016.
Data Mining: Quirita, V.A.A.; Costa, G.A.O.P.; Happ, P.N.; Feitosa, R.Q.; Ferreira, R.S.; Oliveira, D.A.B.; Plaza, A. A New Cloud Computing Architecture for the Classification of Remote Sensing Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 10, p. 409-416, 2017.
Parallelism Analysis: Costa, G.A.O.P; Bentes, C.; Ferreira, R.S.; Feitosa, R.Q.; Oliveira, D.A.B. Exploiting Different Types of Parallelism in Distributed Analysis of Remote Sensing Data. IEEE Geoscience and Remote Sensing Letters, v. 14, p. 1-5, 2017.