ZIP Code boundaries in Chicago. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ).

A dataset containing zip codes in San Jose, California, and their respective populations.

Vector polygon map data of city limits from Houston, Texas containing 731 features.

City limits GIS (Geographic Information System) data provides valuable information about the boundaries of a city, which is crucial for various planning and decision-making processes. Urban planners and government officials use this data to understand the extent of their jurisdiction and to make informed decisions regarding zoning, land use, and infrastructure development within the city limits.

By overlaying city limits GIS data with other layers such as population density, land parcels, and environmental features, planners can analyze spatial patterns and identify areas for growth, conservation, or redevelopment. This data also aids in emergency management by defining the areas of responsibility for different emergency services, helping to streamline response efforts during crises..

This city limits data is available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

This web map displays the California Department of Education's (CDE) core set of geographic data layers. This content represents the authoritative source for all statewide public school site locations and school district service areas boundaries for the 2018-19 academic year. The map also includes school and district layers enriched with student demographic and performance information from the California Department of Education's data collections. These data elements add meaningful statistical and descriptive information that can be visualized and analyzed on a map and used to advance education research or inform decision making.

Firmographic Data for Company Intelligence, B2B Segmentation & KYB Firmographic data is the backbone of modern B2B decision-making, powering everything from lead scoring and segmentation to compliance, financial benchmarking, and market expansion planning. Canaria’s enriched Firmographic Data product delivers deep visibility into U.S. companies by combining standardized insights on revenue ranges, employee count, and business category with optional location verification through Google Maps metadata.

This clean and analysis-ready firmographic dataset is built for precision. Every record is structured, normalized, and deduplicated to support automated workflows across CRMs, BI dashboards, compliance tools, financial models, and sales platforms. Updated weekly, our firmographic data ensures that teams stay ahead of organizational shifts, while providing the matchability and granularity required to fuel market intelligence at scale.

If you're working with fragmented company information, incomplete lead lists, or outdated third-party data, Canaria’s Firmographic Data bridges the gap between surface-level signals and operational insight.

Use Cases: What This Firmographic Data Solves Canaria’s firmographic data offering is used by sales, risk, finance, compliance, and strategy teams to strengthen daily operations, strategic planning, and automation initiatives.

Company Analysis • Leverage firmographic data to assess a company’s size, structure, and potential impact within its industry • Identify organizational tiers using clean employee size brackets, location counts, and business hierarchy insights • Analyze firmographic profiles at the branch level, matched with Google Maps data to verify presence, operating hours, and reviews • Map the operational footprint of enterprises across ZIP codes, cities, and regions for trend tracking or competitive benchmarking

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What Makes This Firmographic Data Unique Deep Enrichment with Verified Firmographic Attributes • Our firmographic data includes revenue range, employee size bracket, industry classification, company type, and regional identifiers — all normalized to enable aggregation, filtering, and modeling.

Matchable with Google Maps for Accuracy and Context • Match your firmographic records with Google Maps to verify physical branch presence, exact addresses, latitude/longitude, phone numbers, and ratings. This adds a real-world signal layer to abstract company data and supports KYB, lead scoring, and risk assessment.

Continuously Updated and Scalable • Weekly refreshes ensure your firmographi...

The SEN12TP dataset (Sentinel-1 and -2 imagery, timely paired) contains 2319 scenes of Sentinel-1 radar and Sentinel-2 optical imagery together with elevation and land cover information of 1236 distinct ROIs taken between 28 March 2017 and 31 December 2020. Each scene has a size of 20km x 20km at 10m pixel spacing. The time difference between optical and radar images is at most 12h, but for almost all scenes it is around 6h since the orbits of Sentinel-1 and -2 are shifted like that. Next to the \(\sigma^\circ\) radar backscatter also the radiometric terrain corrected \(\gamma^\circ\) radar backscatter is calculated and included. \(\gamma^\circ\) values are calculated using the volumetric model presented by Vollrath et. al 2020.

The uncompressed dataset has a size of 222 GB and is split spatially into a train (~90%) and a test set (~10%). For easier download the train set is split into four separate zip archives.

Please cite the following paper when using the dataset, in which the design and creation is detailed:
T. Roßberg and M. Schmitt. A globally applicable method for NDVI estimation from Sentinel-1 SAR backscatter using a deep neural network and the SEN12TP dataset. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 2023. https://doi.org/10.1007/s41064-023-00238-y.

The file sen12tp-metadata.json includes metadata of the selected scenes. It includes for each scene the geometry, an ID for the ROI and the scene, the climate and land cover information used when sampling the central point, the timestamps (in ms) when the Sentinel-1 and -2 image was taken, the month of the year, and the EPSG code of the local UTM Grid (e.g. EPSG:32643 - WGS 84 / UTM zone 43N).

Naming scheme: The images are contained in directories called {roi_id}_{scene_id}, as for some unique regions image pairs of multiple dates are included. In each directory are six files for the different modalities with the naming {scene_id}_{modality}.tif. Multiple modalities are included: radar backscatter and multispectral optical images, the elevation as DSM (digital surface model) and different land cover maps.

Data modalities

name	Modality	GEE collection
s1	Sentinel-1 radar backscatter	COPERNICUS/S1_GRD
s2	Sentinel-2 Level-2A (Bottom of atmosphere, BOA) multispectral optical data with added cloud probability band	COPERNICUS/S2_SR COPERNICUS/S2_CLOUD_PROBABILITY
dsm	30m digital surface model	JAXA/ALOS/AW3D30/V3_2
worldcover	land cover, 10m resolution	ESA/WorldCover/v100

The following bands are included in the tif files, for an further explanation see the documentation on GEE. All bands are resampled to 10m resolution and reprojected to the coordinate reference system of the Sentinel-2 image.

Modality Bands

Modality	Band count	Band names in tif file	Notes
s1	5	VV_sigma0, VH_sigma0, VV_gamma0flat, VH_gamma0flat, incAngle	VV/VH_sigma0 are the \(\sigma^\circ\) values, VV/VH_gamma0flat are the radiometric terrain corrected \(\gamma^\circ\) backscatter values incAngle is the incident angle
s2	13	B1, B2, B3, B4, B5, B7, B7, B8, B8A, B9, B11, B12, cloud_probability	multispectral optical bands and the probability that a pixel is cloudy, calculated with the sentinel2-cloud-detector library optical reflectances are bottom of atmosphere (BOA) reflectances calculated using sen2cor
dsm	1	DSM	Height above sea level. Signed 16 bits. Elevation (in meter) converted from the ellipsoidal height based on ITRF97 and GRS80, using EGM96†1 geoid model.
worldcover	1	Map	Landcover class

Checking the file integrity
After downloading and decompression the file integrity can be checked using the provided file of md5 checksum.
Under Linux: md5sum --check --quiet md5sums.txt

References:

Vollrath, Andreas, Adugna Mullissa, Johannes Reiche (2020). "Angular-Based Radiometric Slope Correction for Sentinel-1 on Google Earth Engine". In: Remote Sensing 12.1, Art no. 1867. https://doi.org/10.3390/rs12111867.