- ArcGIS
- JavaScript
- UAV
- .Net
- Python
Python
Project objective was to help a major GIS solutions provider build a data processing center to enhance their map-making capabilities in Eastern Europe.
The geospatial solutions provider already had data that covered a multitude of countries on all continents and faced growing demand, but they lacked data about the Eastern European region. To provide a full spectrum of geographic information to their users, the GIS provider needed to find a strategic partner in Eastern Europe able to set up an offshore center to process geographic data quickly and cost-effectively. The data processing center had to be completely dedicated to the client, and required competent and technically skilled staff to return a highly accurate product. It had to be fully secured to ensure data protection, and it had to be scalable to match the scope of each project.
In order to effectively compete with existing geospatial data providers, our client needed a GIS QA team to control, verify and manage the data output of other organizations. The increase of GIS use in environmental modeling and the availability of a variety of digital datasets raised concerns about the quality of end-products. Our client needed a partner capable of verifying and delivering proven, quality data. Their existing project used municipal source civic address maps that were converted to vector GIS files of address points, and utilized satellite imagery that enhanced positioning. These vector files required QA resources to fix problems where necessary. Despite the fact that our client is experienced in spatial analysis and geospatial data processing, they needed an extra set of resources to ensure integrity of their data and ongoing quality of their services.
In creating a specialized GIS of the flooded Ukrainian territories on the QGIS platform, it was necessary to shift the existing process from processing data using paper questionnaires to GIS processing.
Project objective was to help a cadaster agency update interactive land use maps for better land resources administration and management.
The client was dissatisfied with existing land use maps, but did not have the geo-expertise to improve the maps internally. They wanted to update the information contained in the interactive land distribution maps that are frequently used by farmers and land owners to monitor land use coverage. The client was looking for a partner to revise existing data and find latest land use and land cover data of a specific area without onsite investigation. They needed a partner able to analyze remote sensing data and satellite images, organize data, and map the results.
A leading provider of navigable maps needed to perform spatial analysis on a large amount of data in order to deliver high quality map services to its customers. The existing application used during the client’s production did not support automatic data analysis and was not able to synchronize data with existing GIS systems. To solve these challenges, they needed a reliable partner with experience in building complex GIS solutions.
Project objective was to source and clean geospatial data and create a map of parks and recreation areas in major US cities, which will enrich the provider’s app, enabling users to make better location decisions.
The client produces detailed hyper-local analyses and informative visuals using urban analytics. Their product is an application that makes location-based decisions, such as property investment or new store location, quicker and easier. The client’s main focus is software development and algorithmization, but this project involved atypical tasks for them: collecting different types of spatial and non-spatial data from a variety of open sources and processing them to create consistent and accurate maps. While part of the data could be taken from open public sources and licensed, some data had to first be collected and structured appropriately.
Project objective was to develop and integrate a plugin that requests WMS imagery tiles for selected territories for one of the largest cartographical companies in Ukraine.
As part of participation in a tender, Ukraine’s largest map maker had to perform a pilot project that involved creation of electronic maps. The client needed to act quickly to show new potential customers the effectiveness of their work. While they were satisfied with the software they were using, there was some room for improvement.
- PostGIS
- GeoServer
- Leaflet
- C++
- QGIS
- PostgreSQL
- GDAL
- LIDAR
- Qt
- Java
- Civil 3D
- MapXtreme
- PHP
- MySQL
- GNSS
- GPS
- DotSpatial
- GlobalMapper
- SpatialLite
- MapInfo
- CityEngine
- Inpho
- Orfeo ToolBox
- Machine learning
Commercial vs. Open Source: A comparison of GIS Software
We are conditioned to think that high price equals high value. Because of this, we always assume that something we’ve paid for is better than something free. But is that true in every case?