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We have implemented an updated Hierarchical Triangular Mesh (HTM) as the basis for a unified data model and an indexing scheme for geoscience data to address the variety challenge of Big Earth Data. In the absence of variety, the volume challenge of Big Data is relatively easily addressable with parallel processing. The more important challenge in achieving optimal value with a Big Data solution for...
Plantation mapping is important for understanding deforestation and climate change. Most existing plantation products rely heavily on visual interpretation of satellite imagery, which results in both false positives and false negatives. In this paper we aim to design an automatic framework that map plantations in large regions. Conventional classification methods cannot be directly applied due to...
With the growth of the economy, the air quality is becoming a serious issue, especially for those developing countries, such as China. Therefore, it is very important for the public and the government to access real-time air quality information. Unfortunately, the limited number of air quality monitoring stations is unable to provide fine-grained air quality information in a huge city, such as Beijing...
Monitoring biomass over large geographic regions for changes in vegetation and cropping patterns is important for many applications. Changes in vegetation happen due to reasons ranging from climate change and damages to new government policies and regulations. Remote sensing imagery (multi-spectral and multi-temporal) is widely used in change pattern mapping studies. Existing bi-temporal change detection...
The need for effective change detection is ever growing with more emerging large-scale spatial-temporal datasets that contain gridded time series data. To detect meaningful changing events with respect to our desired characteristics, in this paper we focus on the post-classification change detection problem which aims to apply change detection techniques on the time series of classification outputs...
Concerns for environmental sustainability are leading to a growing interest understanding geospatial data. At the same time, the availability of high-resolution imagery from satellites and unmanned air systems is increasing rapidly. However, the processing techniques that are available within geographic information systems are not yet adapted to big data challenges and opportunities. We propose a...
Advances in satellite imagery presents unprecedented opportunities for understanding natural and social phenomena at global and regional scales. Although the field of satellite remote sensing has evaluated imperative questions to human and environmental sustainability, scaling those techniques to very high spatial resolutions at regional scales remains a challenge. Satellite imagery is now more accessible...
IBM's Physical Analytics Integrated Data Repository and Services (PAIRS) is a geospatial Big Data service. PAIRS contains a massive amount of curated geospatial (or more precisely spatio-temporal) data from a large number of public and private data resources, and also supports user contributed data layers. PAIRS offers an easy-to-use platform for both rapid assembly and retrieval of geospatial datasets...
Clustering can help to make large datasets more manageable by grouping together similar objects. However, most clustering approaches are unable to scale to very large datasets (e.g. more than 10 million objects). The K-Tree is a data structure and clustering algorithm that has proven to be scalable with large streaming datasets. Here, we apply the K-Tree to spatial data (satellite images) and extend...
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