An automated machine learning framework for geospatial data named PAIRS AutoGeo is introduced on IBM PAIRS Geoscope big data and analytics platform. The frame-work simplifies the development of industrial machine learning solutions leveraging geospatial data to the extent that the user inputs are minimized to merely a text file containing labeled GPS coordinates. PAIRS AutoGeo automatically gathers required data at the location coordinates, assembles the training data, performs quality check, and trains multiple machine learning models for subsequent deployment. The framework is validated using a realistic industrial use case of tree species classification. Open-source tree species data are used as the input to train a random forest classifier and a modified ResNet model for 10-way tree species classification based on aerial imagery, which leads to an accuracy of 59.8% and 81.4%, respectively. This use case exemplifies how PAIRS AutoGeo enables users to leverage machine learning without extensive geospatial expertise.

Accurately and globally mapping human infrastructure is an important and challenging task with applications in routing, regulation compliance monitoring, and natural disaster response management etc.. In this paper we present progress in developing an algorithmic pipeline and distributed compute system that automates the process of map creation using high resolution aerial images. Unlike previous studies, most of which use datasets that are available only in a few cities across the world, we utilizes publicly available imagery and map data, both of which cover the contiguous United States (CONUS). We approach the technical challenge of inaccurate and incomplete training data adopting state-of-the-art convolutional neural network architectures such as the U-Net and the CycleGAN to incrementally generate maps with increasingly more accurate and more complete labels of man-made infrastructure such as roads and houses. Since scaling the mapping task to CONUS calls for parallelization, we then adopted an asynchronous distributed stochastic parallel gradient descent training scheme to distribute the computational workload onto a cluster of GPUs with nearly linear speed-up.

The assessment of the vegetation growth rate given remote sensing data is a challenging task in the Earth Observation sciences. LiDAR data acquisition is commonly used to extract height information at a given moment in time, however, the associated cost and complexity restrict continuous acquisitions. Frequently captured aerial imagery can be used to identify and separate vegetation from bare land, water, impervious surface, or built infrastructure. A combination of LiDAR data with aerial and radar imagery allows to track dynamic seasonal growth of vegetation around critical infrastructure such as power lines. We present a general framework that integrates tree identification and growth assessment around power lines with the goal to identify locations of high risk where trees potentially cause power outages.

We present an efficient clustering algorithm applicable to one-dimensional data such as e.g. a series of times-tamps. Given an expected frequency ΔT-1, we introduce an O(N)-efficient method of characterizing N events represented by an ordered series of timestamps t1, t2,..., tN. In practice, the method proves useful to e.g. identify time intervals of missing data or to locate isolated events. Moreover, we define measures to quantify a series of events by varying ΔT to e.g. determine the quality of an Internet of Things service.

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 or performing complex analytics, lowering time-to-discovery significantly by reducing the data curation and management burden. In this paper, we review recent progress with PAIRS and showcase a few exemplary analytical applications which the authors are able to build with relative ease leveraging this technology.

Solar car race competitions offer realistic conditions to test and demonstrate the state-of-the-art technologies in multidisciplinary fields. In such races the solar panels mounted on the car produce the energy required to power the vehicle. A simulator runs during the race determines the optimal race speed based on the predicted availability of solar energy and other parameters as well as road conditions. The accuracy of the forecasts, especially the solar irradiance forecasts, has a significant impact on the race strategy. Here we report on the experience of providing irradiance forecasts for two races run by the University of Michigan Solar Car Team at the Bridgestone World Solar Challenge 2015 in Australia and at the American Solar Challenge 2016 from Ohio to South Dakota. The probabilistic forecasts of hourly solar irradiance generated from machine learning algorithms were deployed to optimally decide on the race strategy. This work showcases an example of real time decision making based on insights derived from machine learning utilizing big geospatial data — weather models and measurement data from weather station networks.

Geospatial data volume exceeds hundreds of Petabytes and is increasing exponentially mainly driven by images/videos/data generated by mobile devices and high resolution imaging systems. Fast data discovery on historical archives and/or real time datasets is currently limited by various data formats that have different projections and spatial resolution, requiring extensive data processing before analytics can be carried out. A new platform called Physical Analytics Integrated Repository and Services (PAIRS) is presented that enables rapid data discovery by automatically updating, joining, and homogenizing data layers in space and time. Built on top of open source big data software, PAIRS manages automatic data download, data curation, and scalable storage while being simultaneously a computational platform for running physical and statistical models on the curated datasets. By addressing data curation before data being uploaded to the platform, multi-layer queries and filtering can be performed in real time. In addition, PAIRS offers a foundation for developing custom analytics. Towards that end we present two examples with models which are running operationally: (1) high resolution evapo-transpiration and vegetation monitoring for agriculture and (2) hyperlocal weather forecasting driven by machine learning for renewable energy forecasting.