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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 and obtain enough storage space for raster images, and streamline image data pre-processing workflows before images can be visualized within web applications. TiTiler, or Tile Server for Cloud Optimized GeoTIFFs or

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

Today, geospatial applications can process enormous volumes of raster images, including satellite images, aerial surveys, drone imagery, and scanned maps. The ability to manage these large datasets efficiently requires advanced image-encoding and decoding techniques, which the MrSID decoding SDK provides to developers. The MrSID Decoding SDK was developed by LizardTech; it allows developers to build fast, scalable, and reliable capabilities into desktop, server, cloud, and mo

The aerial mapping programs across the country generate tremendous amounts of image data annually. Agencies at both the state and federal levels, as well as geospatial companies in the commercial sector, require image formats that efficiently store, manage, distribute, and analyze terabytes (or even petabytes) of raster image data with minimal degradation in quality or performance. The choice of image format has a significant effect on costs of storage, processing speed, inte

LiDAR technology plays an important role in many sectors, such as geospatial mapping, forestry, urban planning, and autonomous systems. One of the main challenges for these professionals using lidar point cloud datasets is that they can be extremely large. Thus, they need to use compression, as this aids in how to store, send, and maintain strong performance out of the box. In this article, we will discuss where to find reliable hiding spots. We will also look at desirable fe

Across many different industries, including defense and intelligence, environmental monitoring, urban planning, and precision agriculture, satellite images have an important role to play. The many ways they are used to manage large quantities of high-resolution geospatial (GIS) data pose a challenge to those who must store, transmit, and use the data in an efficient way. To help solve these problems, satellite image compression is necessary. Why Satellite Imagery Compression

Organizations are increasingly dependent on large volumes of high-resolution image data, including satellite and aerial photography, medical scan data, and geospatial data, to drive their decision-making processes. As image sizes continue to grow, storing, transmitting, and processing them will create significant challenges. This is where wavelet-based compression is an excellent option to solve these three problems. Wavelet-based Compression What is Wavelet-Based Compression

Geospatial information and Earth observation (EO) are enjoying an unprecedented period of advancement. With the increased availability of satellites, drones, and airborne imaging capabilities, we collect more information about the Earth than ever before. Virtually every industry is benefiting from the ability to use high-resolution images for making more informed decisions in areas as diverse as environmental monitoring, urban planning, or military intelligence. With higher r

Selecting an appropriate compression scheme for large raster datasets, including satellite imagery, aerial photography, and scanned maps, is essential when working with huge datasets. There are two popular formats utilized in the geospatial field: MrSID (Multi-resolution Seamless Image Database) and ECW (Enhanced Compression Wavelet). Even though both MrSID and ECW are effective for handling large images, they vary widely in performance, scalability, licensing, and long-term

As the amount of geospatial data continues to increase significantly and cloud-based workflows have become commonplace, selecting the correct format for geospatial data has grown increasingly important. Two formats that are frequently discussed in relation to modern digital geospatial/scientific data systems are Cloud Optimized GeoTIFFs ('COG') and Zarr. While both formats are intended to promote effective access and usage of very large datasets, each is designed for differen

Nowadays, the geospatial industry is managing larger raster datasets than ever before. Raster datasets are managed from aerial photography, satellite imagery, and orthophotography imagery, but it is necessary to get a balance between how much it costs to store them, how quickly they will perform, and how visually pleasing they are. To compare the performance of the two widely used raster data storage formats, Cloud Optimized GeoTIFF (COG) and MrSID, we have conducted a side-b

When it comes to storing and delivering images in the modern data-driven geo-spatial world, there is now a plethora of formats available for high-resolution satellite images to orthophotomosaics via drones . The massively increasing amounts and complexity of raster data are overwhelming, and as a result, two recognised formats for use are MrSID and Zarr; although both file types have a different intended purpose and application. MrSID vs Zarr What is MrSID? MrSID [while co