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The analysis of geospatial data is one of the critical components in GIS, remote sensing, environmental monitoring, urban planning, and location-based services. With the increasing complexity and amount of geospatial data, Python has become one of the languages used for the processing and automation of the data and development of geospatial solutions. One of the tools or packages used in Python geospatial workflows – PyCell – can be discussed in the field of geospatial data p

Modern data analytics demands the use of tools capable of working efficiently with big data, without any additional infrastructure or complexity in the database management system. In the course of analyzing structured data, CSV files, Parquet datasets, and analytical workloads, there appears to be a demand for lightweight database engines. DuckDB is an open-source analytical database, capable of executing sophisticated queries, especially in connection with analytical workloa

Geospatial programming is the use of programming languages and libraries to manipulate, process, analyze, and visualize geographic data. As GIS technology, remote sensing, and spatial analysis become more advanced, developers are considering programming languages that offer high performance, safety features, and efficient handling of resources. Rust programming language is an example of such a language that is suitable for developing efficient software for geospatial programm

The processes related to Geographical Information Systems (GIS) include manipulation and processing of large amounts of spatial data, geospatial analysis, geographic database management, and map production. These activities may become monotonous and time-consuming. ArcPy is a special Python library that helps to automate processes connected with GIS and perform various geoprocessing procedures in ArcGIS. ArcPy is developed by the company Esri and includes many tools for geopr

Interactive maps are an essential feature of modern geospatial data visualization tools. Interactive maps offer users the ability to analyze geospatial data and understand spatial patterns, locations, and relationships between various places better than static maps. There are a number of libraries that provide users with the ability to build interactive maps in Python. One such library is called PyDeck. PyDeck is a Python library that acts as a Python interface to Deck.GL, a

Processing of geospatial data is one of the processes that demand effective and powerful tools. WhiteboxTools is an open-source geospatial analysis software that is designed to allow efficient processing of geospatial data. The software offers a comprehensive collection of tools for terrain analysis, hydrology, raster processing, LiDAR analysis, and image processing. For GIS analysts, remote sensing analysts, researchers, and developers, WhiteboxTools is capable of automating

Geospatial rasters play a crucial role in GIS, remote sensing, satellite imagery, environmental studies, and spatial modeling. Raster files store geospatial data in cells, where every cell contains values that represent elevation, temperature, precipitation, vegetation indices, or land cover type. Processing of large geospatial raster datasets may be complicated, especially when one deals with multidimensional data, multiband images, and time series satellite imagery. The Pyt

Raster geospatial data finds application in various areas such as geographic information systems (GIS), remote sensing, satellite images, aerial mapping, environmental monitoring, and spatial analysis. Unfortunately, during raster processing, one may accidentally lose or distort some of the vital metadata that includes CRSs, geotransforms, pixel resolution, NoData value, and image acquisition details. This metadata plays a key role in ensuring the accuracy of spatial operatio

LiDAR is a technology used for surveying, mapping, and various engineering and forestry applications that generates 3D point cloud data. But LiDAR datasets can get very big, since each point will have coordinates, elevation, intensity, classification, returns, and many other parameters. Data compression for LiDAR reduces storage and increases speed for data transfer and processing. There are three formats commonly used in geospatial data processing: LAS, LAZ, and MrSID. Even

Patterns in space are an inherent component of geographic analyses. In most GIS and remote sensing processes, knowing only the location of features is not enough; you also need to understand the distribution of their corresponding values—whether they are clustered, dispersed, or randomly spread. Spatial clustering analysis detects statistically significant areas with high or low values in space. PySAL is an open-source Python ecosystem for spatial data science. This framework

MrSID Imagery can now be supported in AutoCAD 2027 using the MrSID Plugin for AutoCAD. This will enable CAD and GIS professionals to use highly compressed geospatial raster data directly in AutoCAD without having to go through complicated imagery conversions. AutoCAD 2027 Supports MrSID What is MrSID? MrSID stands for Multiresolution Seamless Image Database. It is an image file format that enables efficient compression and viewing of large geospatial raster files. The format

It is no exaggeration to say that Python has become one of the most popular programming languages for GIS, spatial analysis, remote sensing, and geospatial data science. The numerous open-source libraries allow processing both vector and raster data, conducting spatial analysis, creating maps, handling satellite images, and automating geospatial workflows. For those who work as GIS analysts, remote sensing experts, data scientists, or developers creating geospatial applicatio

Visualizing geospatial data is essential in GIS, remote sensing, location intelligence, urban planning, environmental monitoring, and spatial analytics. There are many Python libraries that provide geospatial data manipulation features, but when interactive visualizations, filtering, hover data, and browser-based visualization are required, Bokeh can be used. Bokeh is an interactive visualization library in Python that allows building plots and other visualizations on the web

Interactive web maps have been necessary in contemporary applications that deal with Geographic Information Systems (GIS), location intelligence, real-time tracking, environmental monitoring, logistics, urban planning, and spatial data visualization. One of the most robust JavaScript libraries for creating highly interactive web mapping applications is OpenLayers. The present OpenLayers API tutorial describes OpenLayers, its architecture, layers, map sources, and creation of

Image resizing is one of the frequently performed operations in various Python-based projects that use web development, machine learning, computer vision, remote sensing, GIS, photography, or image processing. Resizing an image might be needed to decrease its file size, prepare images for deep learning, make thumbnails, standardize the sizes of the images, or to optimize them for the website. Pillow is one of the most popular Python packages for image manipulation. It offers

The ability to produce reliable 3D models from common photos is important in areas like photogrammetry, computer vision, robotics, etc. Rather than creating the model manually, modern photogrammetry applications can process many overlapping photos and automatically reconstruct the scene geometry. COLMAP is one of the most popular open-source tools for this type of reconstruction. It offers an excellent pipeline that turns images into camera poses, sparse and dense point cloud

Photogrammetry has emerged as a vital tool for the generation of 3D models, point clouds, camera poses, and geospatial data from overlapping photographs. From drones to 3D reconstructions and computer vision, all forms of photogrammetric processes rely on the availability of software packages that can extract geometry from photographs. OpenMVG (Open Multiple View Geometry) is an open-source computer vision library that aims to provide the fundamental algorithms for Multiple V

Geospatial applications typically require conversion between human-readable addresses and geographic coordinates. Nominatim is one of the most widely used geocoding services based on OpenStreetMap (OSM) data, and it can perform both geocoding and reverse geocoding. Nominatim is widely used in GIS applications, web mapping tools, location-based services, Python workflows, and spatial data processing. This technical guide will help readers understand how Nominatim works, how to

Geographic Information Systems (GIS) make extensive use of large amounts of spatial data obtained from satellites, drones, aerial surveys, and remote sensor technologies. Large files such as high-resolution images, raster data, and earth observation data can quickly become terabytes in size, creating difficulties with storage, data transfers, processing speed, and costs related to the cloud. To solve this problem, GIS professionals use techniques for data compression that are

Geographic coordinates are critical when it comes to any modern Geographic Information System (GIS). For instance, whether you are creating a location intelligence platform, doing customer address mapping, analyzing logistics networks, building emergency response solutions, or using AI to create geospatial apps, converting addresses to geographic coordinates is paramount. This is where GeoPy becomes one of the key Python libraries for developers working on GIS applications. I