HyPDS: Enabling a Hybrid File Transfer Protocol and Peer to Peer Content Distribution System for Remote Sensing Data With the rapid development of remote sensing technologies, highresolution remote sensing data has been widely studied and applied in many fields, such as geography, agriculture, and earthquake detection. However, providing users with efficient remote sensing data distribution services is a huge challenge, which is due to the timeliness and the shorter update cycle of remote sensing data. In this paper, we propose HyPDS, a hybrid File Transfer ProtocolPeer to Peer (FTPP2P) content distribution system for remote sensing data. A main problem in the design of a HyPDS content distribution system is therefore how to efficiently utilize the bandwidth of downloading peer nodes so as to maximize utilization rate of the downstream bandwidth of the resource requesting nodes of the system. Using the realworld measurements, we identify the key factor that affects the utilization rate of the downstream bandwidth of the resource requesting nodes and thus construct a finegrained performance model (i.e., P2PFTP) to address the optimal bandwidth allocation problem. We also have deployed HyPDS on the realworld system platform, which has more than 100 Terabytes (TB) of remote sensing data resources in the key Laboratory of Aerospace Information Applications. Experimental results showed that HyPDS has significantly outperformed the stateoftheart approaches in maximizing the bandwidth usage, with a maximum increase of 45%.

Distributed processing platform for large datasets: satellite imagery usecase For most of the programs, the most significant amount of time is spent on running a CPUintensive component, while retrieving and reading the input or formatting the output only takes an insignificant amount of time, especially if both the input and output are structured data. Thus, most of the computer science literature on distributed systems discuss the optimization of algorithms and frequently treat the IO and input preparation parts of the program execution as a much less important one for overall efficiency. That wellinformed decision of ignoring the IO part when discussing runtime optimization might change to a fault when we consider algorithms running over massive datasets that should also be retrieved at a userxe2x80x99s request. In this paper, we propose and analyze an usercentric processing platform for running algorithms over satellite imagery products, including a review of the important challenges, solutions, and opportunities on extracting valuable results from GIS products. The 4th section of the paper includes two examples of reallife usage of remote sensing for the observation of natural habitats. In the 5th section we include three practical experiments assessing different aspects of the proposed solution.