NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors

© 2016 Cheung, Schultz and Luk.NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation

A CyberGIS Integration and Computation Framework for High‐Resolution Continental‐Scale Flood Inundation Mapping

We present a Digital Elevation Model (DEM)-based hydrologic analysis methodology for continental flood inundation mapping (CFIM), implemented as a cyberGIS scientific workflow in which a 1/3rd arc-second (10m) Height Above Nearest Drainage (HAND) raster data for the conterminous U.S. (CONUS) was computed and employed for subsequent inundation mapping. A cyberGIS framework was developed to enable spatiotemporal integration and scalable computing of the entire inundation mapping process on a hybrid supercomputing architecture. The first 1/3rd arc-second CONUS HAND raster dataset was computed in 1.5 days on the CyberGIS ROGER supercomputer. The inundation mapping process developed in our exploratory study couples HAND with National Water Model (NWM) forecast data to enable near real-time inundation forecasts for CONUS. The computational performance of HAND and the inundation mapping process was profiled to gain insights into the computational characteristics in high-performance parallel computing scenarios. The establishment of the CFIM computational framework has broad and significant research implications that may lead to further development and improvement of flood inundation mapping methodologies

Multi-scale building maps from aerial imagery

Nowadays, the extraction of buildings from aerial imagery is mainly done through deep convolutional neural networks (DCNNs). Buildings are predicted as binary pixel masks and then regularized to polygons. Restricted by nearby occlusions (such as trees), building eaves, and sometimes imperfect imagery data, these results can hardly be used to generate detailed building footprints comparable to authoritative data. Therefore, most products can only be used for mapping at smaller map scale. The level of detail that should be retained is normally determined by the scale parameter in the regularization algorithm. However, this scale information has been already defined in cartography. From existing maps of different scales, neural network can be used to learn such scale information implicitly. The network can perform generalization directly on the mask output and generate multi-scale building maps at once. In this work, a pipeline method is proposed, which can generate multi-scale building maps from aerial imagery directly. We used a land cover classification model to provide the building blobs. With the models pre-trained for cartographic building generalization, blobs were generalized to three target map scales, 1:10,000, 1:15,000, and 1:25,000. After post-processing with vectorization and regularization, multi-scale building maps were generated and then compared with existing authoritative building data qualitatively and quantitatively. In addition, change detection was performed and suggestions for unmapped buildings could be provided at a desired map scale. . © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives