Technology

As location-based applications continue to grow, efficiently storing and querying geographic data has become a critical challenge. Whether you're building a GIS platform, a mapping application, a ride-sharing service, or a geospatial analytics system, you need a way to quickly find spatial objects without scanning millions of records. This is where R-Trees come into play. R-Trees are an example of one of the most commonly used structures for storing and querying multi-dimensi

The amount of geospatial data that organizations need to manage is more than ever before, and many organizations are evaluating how to marry their large datasets to the real-time spatial analytics they require, as well as manage complex geographic workflows. In many cases, many of the traditional GIS tools fall short on performance bottlenecks when performing the analysis of millions of geometries, which leads to slow performance and lower productivity from those analyses. Py

Geographical information continues to grow at an explosive rate and become increasingly complex; therefore, a growing issue has arisen: how to serve raster datasets efficiently over the web. Traditional GIS servers need to build out their infrastructure, configure systems, and obtain sufficient storage space for raster images, and streamline image data preprocessing workflows before images can be visualized in web applications. TiTiler, or Tile Server for Cloud Optimized GeoT

Today, modern geospatial analytics require much more than just a few different styles of static coordinate plots. Data Scientists, GIS engineers, and Machine Learning Practitioners are looking for much more than simple maps (i.e., coordinate-based) — they need contextualised maps that use context alongside rich analytics to tell their story geographically. As such, Contextily is an incredible asset to the Python geospatial ecosystem. Contextily is a very lightweight library i

Today’s world relies heavily on geospatial data for a wide range of analyses, including urban development, logistics, environmental monitoring, and so much more. Many Python developers use interactive maps built with various mapping libraries to produce visually rich insights from spatial data. When it comes to handling geospatial data in Python, one library that stands out is Folium, as it lets developers build interactive maps using Leaflet.js without writing HTML or JavaSc

Geographical Information Systems are central to the processes and workflows of today’s data engineering, geospatial analysis, environmental modeling, and location intelligence. Python has one of the richest ecosystems for working with spatial datasets, and Fiona is among the best libraries to process vector GIS data. Whether you are working with shapefiles, GeoJSON, or GeoPackage, Fiona provides light-weight yet Pythonic interfaces for both reading and writing spatial dataset

Accurate coordinate transformations, map projections, and spatial reference systems are critical to geospatial applications. Whether you are developing GIS software, processing satellite imagery, or creating location-based analytics, you must have some understanding of coordinate systems. One of the most powerful libraries for this purpose is PyProj, which is based on the industry-standard library PROJ and allows developers and GIS Engineers to perform precise coordinate conv

Geospatial Data processing is a primary element of today's analytics, with applications in urban planning, environmental monitoring, agriculture, transportation, telecommunications, and disaster management. The most widely used, trusted, and battle-tested libraries within the geospatial ecosystem include GDAL and OGR. GDAL/OGR, combined with Python, provides a high-performance solution for raster and vector geospatial processing. Developers and GIS professionals can use GDAL/

Geospatial raster data is an essential component of many different types of analysis—from satellite imagery and land use classification, to digital elevation models (DEM) and weather forecasting. For developers and GIS professionals using Python, Rasterio is a highly regarded library for working with raster as well as raster-based data. Rasterio is built upon the GDAL database system and provides a simpler and more developer-friendly interface for performing raster operations

Today, GIS and geospatial data processing are key elements of analytic processes in almost every business or industry, including but not limited to: environmental monitoring; urban development; agriculture; logistics; telecommunications; and remote sensing. Among the vast array of programming languages available for GIS and spatial analysis, Python is emerging as one of the most powerful options. A key resource when using Python for GIS and spatial analysis is the Shapely lib

Geospatial data is the fuel behind many applications, including navigation apps, urban planning, environmental monitoring, business intelligence, and all forms of data-driven decision-making. There is an increasing reliance on data-driven decision-making based on geospatial (i.e., location-based) information for organizations now more than ever. Therefore, Python has emerged as one of the most significant programming languages for geospatial analysis. In this guide, you’ll le

Geographic Information Systems (GIS) rely heavily on large raster datasets, including satellite imagery , aerial photography , LiDAR-derived rasters, and scanned maps. These datasets can easily reach gigabytes or even terabytes in size, creating challenges for storage, processing, and sharing. To manage these large files efficiently, GIS professionals often rely on lossy compression formats. These formats significantly reduce file size while preserving enough visual and spat

LizardTech, a GeoWGS84 company, is proud to be moving forward by launching our new and refreshed look. As we look to the future, we have updated our logo and brand, refocused our efforts to create innovative products that help customers meet their technical needs better, and reinforced our continued commitment to providing exceptional technical services and solutions. Even though our look has changed, the values and commitment behind the LizardTech brand remain constant. A Ne

LizardTech, now a GeoWGS84 company, is a leader in technology for managing and compressing geospatial data. On September 20, 2017, LizardTech announced that it had been granted a U.S. Patent for its LiDAR Point Cloud Compression Technology, which represents a tremendous step forward in the way that large LiDAR data sets are stored, transmitted, and processed. This patent recognizes LizardTech’s proprietary method of losslessly compressing LiDAR Point Clouds, which allows an o