Wanfeng Zhang

TL;DR: The proposed infrastructure of multiple data centers (MDC) for managing and processing massive remote sensing images is built on both groups of distributed DCs/clusters, which are equipped with DC or cluster resource manager.

TL;DR: A generic data‐structure oriented programming template to support massive remote sensing data processing in high‐performance clusters and templates that provide distributed abstractions for large remote sensing image data with complex data structure and allow these distributed data to be accessed as a global one are proposed.

TL;DR: A strategy of partitioning group based on hypergraph (PGH) is introduced to formulate the model of sharing files and another scheduling policy, which is called optimized task tree (OTT) strategy, is adopted to handle the DAG workflow of massive remote sensing data processing with data dependencies.

TL;DR: In this article, the classification of urban functional areas based on dual-modal data (i.e., remote sensing image and user behavior data) was implemented using machine learning (ML) algorithms.

TL;DR: A Web 2.0‐based browser/server model is designed for SGMRSIP to enhance the UE and the experimental results showed that the software scalability and interaction were improved and a better UE was achieved, compared with the existing SGM RSIP.