map of all SF properties with associated, zoning, permits, complaints and appeals history

See the Data Downloads section of the websites help page for links to individual DataSF datasets used to create the Property Information Map https://sfplanninggis.org/pim/help.html

This layer contains the boundaries and IDs of the Maryland tax maps produced by Maryland Department of Planning. Tax maps, also known as assessment maps, property maps or parcel maps, are a graphic representation of real property showing and defining individual property boundaries in relationship to contiguous real property.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Layer Link:https://geodata.md.gov/imap/rest/services/PlanningCadastre/MD_PropertyData/MapServer/2

Public view of the parcel layer. This view is limited to only the attributes that can be seen by the general public.The data table includes the following fields: Shape Type (Shape), Shape.STArea() (Shape_Area), Shape.STLength() (Shape_Area), Name (APN), Created By Record (CreatedbyR), Retired By Record (RetiredbyR), Stated Area, Stated Area Unit (StatedAr_1), Calculated Area (Calculated), Misclose Ratio (MiscloseRa), Misclose Distance (MiscloseDi), Is Seed (IsSeed), Created By (created_us), Created Date (created_da), Modified By (last_edite), Modified Date (last_edi_1), Validation Status (VALIDATION), APN Dashed (APN_Dashed), Map Page (Map_Page), Municipality (Municipali), FloorOrder, HideThere are approximately 51,300 real property parcels in Napa County. Parcels delineate the approximate boundaries of property ownership as described in Napa County deeds, filed maps, and other source documents. GIS parcel boundaries are maintained by the Information Technology Services GIS team. Assessor Parcel Maps are created and maintained by the Assessor Division Mapping Section. Each parcel has an Assessor Parcel Number (APN) that is its unique identifier. The APN is the link to various Napa County databases containing information such as owner name, situs address, property value, land use, zoning, flood data, and other related information. Data for this map service is sourced from the Napa County Parcels dataset which is updated nightly with any recent changes made by the mapping team. There may at times be a delay between when a document is recorded and when the new parcel boundary configuration and corresponding information is available in the online GIS parcel viewer.From 1850 to early 1900s assessor staff wrote the name of the property owner and the property value on map pages. They began using larger maps, called “tank maps” because of the large steel cabinet they were kept in, organized by school district (before unification) on which names and values were written. In the 1920s, the assessor kept large books of maps by road district on which names were written. In the 1950s, most county assessors contracted with the State Board of Equalization for board staff to draw standardized 11x17 inch maps following the provisions of Assessor Handbook 215. Maps were originally drawn on linen. By the 1980’s Assessor maps were being drawn on mylar rather than linen. In the early 1990s Napa County transitioned from drawing on mylar to creating maps in AutoCAD. When GIS arrived in Napa County in the mid-1990s, the AutoCAD images were copied over into the GIS parcel layer. Sidwell, an independent consultant, was then contracted by the Assessor’s Office to convert these APN files into the current seamless ArcGIS parcel fabric for the entire County. Beginning with the 2024-2025 assessment roll, the maps are being drawn directly in the parcel fabric layer.Parcels in the GIS parcel fabric are drawn according to the legal description using coordinate geometry (COGO) drawing tools and various reference data such as Public Lands Survey section boundaries and road centerlines. The legal descriptions are not defined by the GIS parcel fabric. Any changes made in the GIS parcel fabric via official records, filed maps, and other source documents are uploaded overnight. There is always at least a 6-month delay between when a document is recorded and when the new parcel configuration and corresponding information is available in the online parcel viewer for search or download.Parcel boundary accuracy can vary significantly, with errors ranging from a few feet to several hundred feet. These distortions are caused by several factors such as: the map projection - the error derived when a spherical coordinate system model is projected into a planar coordinate system using the local projected coordinate system; and the ground to grid conversion - the distortion between ground survey measurements and the virtual grid measurements. The aim of the parcel fabric is to construct a visual interpretation that is adequate for basic geographic understanding. This digital data is intended for illustration and demonstration purposes only and is not considered a legal resource, nor legally authoritative.

description: Parcels and Land Ownership dataset current as of unknown. This data set consists of digital map files containing parcel-level cadastral information obtained from property descriptions. Cadastral features contained in the data set include real property boundary lines, rights-of-way boundaries, property dimensions.; abstract: Parcels and Land Ownership dataset current as of unknown. This data set consists of digital map files containing parcel-level cadastral information obtained from property descriptions. Cadastral features contained in the data set include real property boundary lines, rights-of-way boundaries, property dimensions.

Historic land uses on lots that were vacant, privately owned, and zoned for manufacturing in 2009. Information came from a review of several years of historical Sanborn maps over the past 100 years. When the SPEED 1.0 mapping application was created in 2009, OER had its vendor examine historic land use maps on vacant, privately-owned, industrially-zoned tax lots. Up to seven years of maps for each lot were examined, and information was recorded that indicated industrial uses or potential environmental contamination such as historic fill. Data for an additional 139 lots requested by community-based organizations was added in 2014. Each record represents the information from a map from a particular year on a particular tax lot at that time. Limitations of funding determined the number of lots included and entailed that not all years were examined for each lot.

OSA web map to view State of Colorado property data

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. This layer contains the boundaries and IDs of the Maryland tax maps produced by Maryland Department of Planning. Tax maps - also known as assessment maps - property maps or parcel maps - are a graphic representation of real property showing and defining individual property boundaries in relationship to contiguous real property. Last Updated: Feature Service Layer Link: http://geodata.md.gov/imap/rest/services/PlanningCadastre/MD_PropertyData/MapServer/2 ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

This Image Service of Maryland Property Data allows for the manipulation of the display properties of the Statewide Tax Maps dataset. This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Image Service Link: https://geodata.md.gov/imap/rest/services/PlanningCadastre/MD_PropertyData/ImageServer

To access the tax lot layer you will need to contact the county Assessor's office. ORMAP is a statewide digital cadastral base map that is publicly accessible, continually maintained, supports the Oregon property tax system, supports a multi-purpose land information system, strives to comply with appropriate state and national standards, and will continue to be improved over time.

MassGIS' standardized assessors’ parcel mapping data set contains property (land lot) boundaries and database information from each community's assessor.The data were developed through a competitive procurement funded by MassGIS. Each community in the Commonwealth was bid on by one or more vendors and the unit of work awarded was a city or town. The specification for this work was Level 3 of the MassGIS Digital Parcel Standard.This map service contains three feature classes and one table.Feature service also available.See the datalayer page for full details.

This dataset consists of the 1km raster, percentage target class version of the Land Cover Map 2015 (LCM2015) for Great Britain. The 1km percentage product provides the percentage cover for each of 21 land cover classes for 1km x 1km pixels. This product contains one band per target habitat class (producing a 21 band image). The 21 target classes are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. This dataset is derived from the vector version of the Land Cover Map, which contains individual parcels of land cover and is the highest available spatial resolution. LCM2015 is a land cover map of the UK which was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. LCM2015 consists of a range of raster and vector products and users should familiarise themselves with the full range (see related records, the CEH web site and the LCM2015 Dataset documentation) to select the product most suited to their needs. LCM2015 was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. It is one of a series of land cover maps, produced by UKCEH since 1990. They include versions in 1990, 2000, 2007, 2015, 2017, 2018 and 2019.

To access parcel information:Enter an address or zoom in by using the +/- tools or your mouse scroll wheel. Parcels will draw when zoomed in.Click on a parcel to display a popup with information about that parcel.Click the "Basemap" button to display background aerial imagery.From the "Layers" button you can turn map features on and off.Complete Help (PDF)Parcel Legend:Full Map LegendAbout this ViewerThis viewer displays land property boundaries from assessor parcel maps across Massachusetts. Each parcel is linked to selected descriptive information from assessor databases. Data for all 351 cities and towns are the standardized "Level 3" tax parcels served by MassGIS. More details ...Read about and download parcel dataUpdatesV 1.1: Added 'Layers' tab. (2018)V 1.2: Reformatted popup to use HTML table for columns and made address larger. (Jan 2019)V 1.3: Added 'Download Parcel Data by City/Town' option to list of layers. This box is checked off by default but when activated a user can identify anywhere and download data for that entire city/town, except Boston. (March 14, 2019)V 1.4: Data for Boston is included in the "Level 3" standardized parcels layer. (August 10, 2020)V 1.4 MassGIS, EOTSS 2021

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2023 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2023.Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryWhat can you do with this layer?Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. This layer can also be used in analyses that require land use/land cover input. For example, the Zonal toolset allows a user to understand the composition of a specified area by reporting the total estimates for each of the classes. NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Class definitionsValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

This data set contains a shapefile of a digitized map of the land parcel information of the original properties of the Uruara colonization site, Para, Brazil, acquired from the Instituto de Colonizacao e Reforma Agraria, or the Colonization and Agrarian Reform Institute (INCRA). The Uruara settlement geometry was initially designed by INCRA, and consists of mostly 100 hectare lots (400 x 2500 meters, and 500 x 2000 meters), running north and south of the Trans-Amazon Highway, as a fine network of small, narrow rectangles. The other parcels in the landscape are the so-called glebas that range up to 3,000 hectares.

The map was in the form of a paper map without a projection (a spherical geographic coordinate system) in the South American 1969 datum (SAD 1969). This paper map was digitized in Environmental Science Research Institute (ESRI) ArcInfo 8.1 using a digitizing table, and the digital cadastral data were geo-referenced and projected to match the Universal Transverse Mercator projection (Zone 22 South, World Geodetic System 1984 datum) of Landsat imagery (Landsat.org). There is one compressed (*.zip) file with this data set.

Statewide soil and land information can be discovered and viewed through eSPADE or SEED. Datasets include soil profiles, soil landscapes, soil and land resources, acid sulfate soil risk mapping, hydrogeological landscapes, land systems and land use. There are also various statewide coverages of specific soil and land characteristics, such as soil type, land and soil capability, soil fertility, soil regolith, soil hydrology and modelled soil properties.

Both eSPADE and SEED enable soil and land data to be viewed on a map. SEED focuses more on the holistic approach by enabling you to add other environmental layers such as mining boundaries, vegetation or water monitoring points. SEED also provides access to metadata and data quality statements for layers.

eSPADE provides greater functions and allows you to drill down into soil points or maps to access detailed information such as reports and images. You can navigate to a specific location, then search and select multiple objects and access detailed information about them. You can also export spatial information for use in other applications such as Google Earth™ and GIS software.

eSPADE is a free Internet information system and works on desktop computers, laptops and mobile devices such as smartphones and tablets and uses a Google maps-based platform familiar to most users. It has over 42,000 soil profile descriptions and approximately 4,000 soil landscape descriptions. This includes the maps and descriptions from the Soil Landscape Mapping program. eSPADE also includes the base maps underpinning Biophysical Strategic Agricultural Land (BSAL).

For more information on eSPADE visit: https://www.environment.nsw.gov.au/topics/land-and-soil/soil-data/espade

Background and Data Limitations The Massachusetts 1830 map series represents a unique data source that depicts land cover and cultural features during the historical period of widespread land clearing for agricultural. To our knowledge, Massachusetts is the only state in the US where detailed land cover information was comprehensively mapped at such an early date. As a result, these maps provide unusual insight into land cover and cultural patterns in 19th century New England. However, as with any historical data, the limitations and appropriate uses of these data must be recognized: (1) These maps were originally developed by many different surveyors across the state, with varying levels of effort and accuracy. (2) It is apparent that original mapping did not follow consistent surveying or drafting protocols; for instance, no consistent minimum mapping unit was identified or used by different surveyors; as a result, whereas some maps depict only large forest blocks, others also depict small wooded areas, suggesting that numerous smaller woodlands may have gone unmapped in many towns. Surveyors also were apparently not consistent in what they mapped as ‘woodlands’: comparison with independently collected tax valuation data from the same time period indicates substantial lack of consistency among towns in the relative amounts of ‘woodlands’, ‘unimproved’ lands, and ‘unimproveable’ lands that were mapped as ‘woodlands’ on the 1830 maps. In some instances, the lack of consistent mapping protocols resulted in substantially different patterns of forest cover being depicted on maps from adjoining towns that may in fact have had relatively similar forest patterns or in woodlands that ‘end’ at a town boundary. (3) The degree to which these maps represent approximations of ‘primary’ woodlands (i.e., areas that were never cleared for agriculture during the historical period, but were generally logged for wood products) varies considerably from town to town, depending on whether agricultural land clearing peaked prior to, during, or substantially after 1830. (4) Despite our efforts to accurately geo-reference and digitize these maps, a variety of additional sources of error were introduced in converting the mapped information to electronic data files (see detailed methods below). Thus, we urge considerable caution in interpreting these maps. Despite these limitations, the 1830 maps present an incredible wealth of information about land cover patterns and cultural features during the early 19th century, a period that continues to exert strong influence on the natural and cultural landscapes of the region. Acknowledgements Financial support for this project was provided by the BioMap Project of the Massachusetts Natural Heritage and Endangered Species Program, the National Science Foundation, and the Andrew Mellon Foundation. This project is a contribution of the Harvard Forest Long Term Ecological Research Program.

Mineral Land Classification studies are produced by the State Geologist as specified by the Surface Mining and Reclamation Act (SMARA, PRC 2710 et seq.) of 1975. To address mineral resource conservation, SMARA mandated a two-phase process called classification-designation. Classification is carried out by the State Geologist and designation is a function of the State Mining and Geology Board. The classification studies contained here evaluate the mineral resources and present this information in the form of Mineral Resource Zones. The objective of the classification-designation process is to ensure, through appropriate local lead agency policies and procedures, that mineral materials will be available when needed and do not become inaccessible as a result of inadequate information during the land-use decision-making process.

This is the land parcels (polygon) dataset for the UKCEH Land Cover Map of 2019 (LCM2019) representing Great Britain. It describes Great Britain's land cover in 2019 using UKCEH Land Cover Classes, which are based on UK Biodiversity Action Plan broad habitats. This dataset was derived from the corresponding LCM2019 20m classified pixels dataset. All further LCM2019 datasets for Great Britain are derived from this land parcel product. A range of land parcel attributes are provided. These include the dominant UKCEH Land Cover Class given as an integer value, and a range of per-parcel pixel statistics to help to assess classification confidence and accuracy; for a full explanation please refer to the dataset documentation. LCM2019 represents a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2019. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2019. LCM2019 was simultaneously released with LCM2017 and LCM2018. These are the latest in a series of UKCEH land cover maps, which began with the 1990 Land Cover Map of Great Britain (now usually referred to as LCM1990) followed by UK-wide land cover maps LCM2000, LCM2007 and LCM2015. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.