Come learn about the latest in OpenLayers developments. The 5.0 release represents a major overhaul of the library, with the goal of improving application developer experience. We'll show you what's new and how you can take advantage of it in your mapping applications. This will be a demo driven talk, walking through examples that highlight new and evolving features of the library.
The increasing availability of large-scale cloud computing resources has enabled large-scale environmental predictive models such as the National Water Model (NWM) to be run essentially continuously. Such models generate so many predictions that the output alone presents a big data computing challenge to interact with and learn from.
The American Community Survey (ACS) annually collects and disseminates a wealth of demographic and economic data, including race, sex, age, occupation, household income, commuting behavior, disability status, etc. The US Census Bureau exposes ACS data via API and the R packages tidycensus automates the downloading and joining of Census geographies and demographic data. This workshop will introduce the users to the data available in ACS, the use of R and RStudio, the tidycensus package for data downloading, and the tmap pakcage for thematic mapping of selected ACS variables.
Classification of the crop fields based on productivity zones can help the crop producers/researchers to apply different management practices based on the variability of productivity across the field. The Site Selection API (SST) is a tool that classifies an area of interest using Enhanced Vegetation Index (EVI) derived from multi spectral images. Using Python open-source packages, it utilizes both Web Feature Service (WFS) and Web Coverage Service (WCS) to explore and retrieve available EVI raster data intersecting the input area of interest (AOI).
At Development Seed, we built a toolbox, Pixel Decoder, that contains multiple ready-to-go algorithms for semantic segmentation. Pixel Decoder’s built in algorithms will help users efficiently integrate machine learning algorithms into their workflow. Semantic segmentation is an area of machine learning focused on partitioning an image into semantically meaningful parts and then classifying each part into a pre-determined class.
CARTO is a successful service and Open Source product stack used by all kinds of organizations. Its portfolio is mainly divided into two main products: BUILDER and ENGINE. This talk is about the latter, exploring how to use the CARTO platform to develop your geospatial applications with a complete set of services, APIs, and client components.
Monsanto’s data scientists attempting to make Web Coverage Service (WCS) calls face poor performance for large raster datasets (high resolution, high-bit depth, and/or large area). In such cases, WCS most often crashes or times out. Raster Direct is a Python package/CLI tool that enables the users to access raster data without size limits. This package is built by using Python open-source packages.
This presentation will highlight the technical side of our successful live data streaming pipeline from a single source (on premise) Oracle Spatial Database to an integrated open source geospatial platform in the cloud (PostGIS). We will describe in detail the technical implementation of the geometry pipeline and its integration into Monsanto’s Enterprise Geospatial Platform in the cloud.
At Monsanto, we gather several types and formats of images from multiple platforms and sensors. In the past, we developed individual processes and services for each imagery sources. For example, manned aerial images (MAV) were processed by one team for a purpose, while satellite imagery was processed by another team for an entirely different purpose.
FOSS4G Conferences, whether regional - like the North American event - or the Global event which took place in Boston in 2017 are important showcase events for the free and open source software for geospatial communities. This presentation will provide an overview of what you can expect at the St. Louis event, but it will also make the case for why investing in the conference - whether through your registration, sponsorship, leadership of a workshop, or other volunteer time - is both crucial and productive.
This paper describes a novel method to compute plot boundaries from real-time GPS data that are directly collected and streamed from cassette planters. In the past, working and visualizing large volume of real-time plots across many geographical areas was a major challenge for large scale previsioning farming and its related business. This challenge is not solely made up by the complicated farming workflows, but also raised by the hardware limits, multiple data and sensor models, and unpredictable environment.
This session will review the state of MapStory - the atlas of change that everyone can edit. Since 2012 when MapStory first debuted at Foss4g-NA, the nonprofit MapStory Foundation has worked with a wide range of open-source geo partners to continually improve the open source, open data MapStory platform. With MapStory, both expert and beginner mappers can create a free account and work together to map the world as it evolves geographically, at local, regional or global scales.
In this talk, I will introduce Radiant.Earth’s platform and present sample use cases around sustainable development goals that can be built using the platform. Radiant.Earth’s platform is open, neutral, collaborative, federated and agile. Our goal is to connect people worldwide to Earth imagery, geospatial data, tools, and knowledge to meet the world’s most critical challenges.
Starting from the context of open source geospatial python development, we will take a deep dive in how to use python development tools and devops tools to build a productive multi-developer python project.
LIDAR, or “Light Detection And Ranging” is a technology which generates 3D point clouds by bouncing signal off objects. It can be deployed from satellite, airplane, drone or ground-mounted sensors, and generates hundreds of millions to billions of data points. Such datasets can overwhelm conventional visualization tools and databases. For example, Florida has comprehensively mapped its coastal zones with aerial LIDAR, an open dataset of several billion points. These are managed as thousands of arbitrarily-tiled files, and must often be heavily pre-processed even to open in GIS.
This paper introduced a new method for assessing housing market value based on the real-time viewshed analysis. Unlike most of existing online viewshed tools, the viewshed tool in this study uses Bresenham's line interpolation to get line-of-sight result, and several constraints were also added to the viewshed algorithm in order to make it more scalable with higher resolution data. A prototype is developed from several most popular open sourced GIS suite, and it shows the potential to include more complicated 3D analysis in online tools in the future.
2017 saw critical outages and interruptions that affected air- and maritime-traffic: hurricanes, computer outages, physical threats, and more. In this talk, we show how these events can be detected using rapidly-developed geo-temporal analytics in Zeppelin and Jupyter notebooks, all backed by GeoMesa (and visualized in Stealth).
Key points to review include:
Detection and reconstruction of 3D buildings in urban areas has been a hot topic of research due to its many applications, including 3D population density studies, emergency planning, and building value estimation. Standard approaches to extract building footprint and measure building height rely on either aerial or space borne point cloud data, which in many areas is unavailable. In contrast, high resolution satellite imagery has become more readily available in recent years, and could provide enough information to estimate a building’s height.
The Cloud Optimized GeoTIFF (COG) is a key component to enabling cloud-native geospatial workflows. An important benefit is the ability for the client to stream just the portion of data that is needed through the use of HTTP GET byte-range requests. COGs enable faster reading, writing, and processing of data on the cloud without the need for local copies.
The Map Algebra Modeling Language (MAML), originally developed at Azavea to expose complex raster processing through a graphical interface, allows clients to craft programs through the direct construction and manipulation of map algebra expression trees.