This resources contains PDF files and Python notebook files that demonstrate how to create geospatial resources in HydroShare and how to use these resources through web services provided by the built-in HydroShare GeoServer instance. Geospatial resources can be consumed directly into ArcMap, ArcGIS, Story Maps, Quantum GIS (QGIS), Leaflet, and many other mapping environments. This provides HydroShare users with the ability to store data and retrieve it via services without needing to set up new data services. All tutorials cover how to add WMS and WFS connections. WCS connections are available for QGIS and are covered in the QGIS tutorial. The tutorials and examples provided here are intended to get the novice user up-to-speed with WMS and GeoServer, though we encourage users to read further on these topic using internet searches and other resources. Also included in this resource is a tutorial designed to that walk users through the process of creating a GeoServer connected resource.

The current list of available tutorials: - Creating a Resource - ArcGIS Pro - ArcMap - ArcGIS Story Maps - QGIS - IpyLeaflet - Folium

sratch layer as wms and then as wfs

In order to digitally plan a cable line using the QGIS plugin ‘Seilaplan’, maps with various background information are helpful. In this tutorial we show you how to obtain maps that are helpful for cable line planning, for example a national map of Switzerland at different scales, the NFI vegetation height model or the NFI forest mix rate. For this we explain what WMS datasets are and how to integrate them into QGIS. No download of large data is needed for this, only a good internet connection. Please note that the tutorial language is German! Link for the integration of WMS data: https://wms.geo.admin.ch/ Link to the description on the Swisstopo website: https://www.geo.admin.ch/en/geo-services/geo-services/portrayal-services-web-mapping/web-map-services-wms.html Link to the Seilaplan website: https://seilaplan.wsl.ch

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung sind verschiedene Hintergrundkarten hilfreich. In diesem Tutorialvideo zeigen wir, was WMS Daten sind und wie man diese in QGIS einbinden kann. Dafür müssen die Daten nicht heruntergeladen werden. Es braucht lediglich eine gute Internetverbindung. Für die Seillinienplanung hilfreiche Karten sind bspw. die Landeskarte der Schweiz in verschiedenen Massstäben, das Vegetationshöhenmodell LFI oder der Waldmischungsgrad LFI. Link zur Einbindung der WMS Daten: https://wms.geo.admin.ch/ Link zur Beschreibung auf der Swisstopo Webseite: https://www.geo.admin.ch/de/geo-dienstleistungen/geodienste/darstellungsdienste-webmapping-webgis-anwendungen/web-map-services-wms.html Link zur Seilaplan-Website: https://seilaplan.wsl.ch

Erstellt vom GIS Kompetenzzentrum der Südtiroler Informatik AG Seit einiger Zeit, spätestens jedoch mit der Veröffentlichung des Geoportals Südtirol (URL: geoportal.buergernetz.bz.it) werden Internet-Kartendienste für jedermann sichtbar, zugänglich und verwendbar. Die Standardisierung wird mit den in den technischen Dokumenten des Open Geospatial Consortiums (OGC) beschriebenen Regeln und Normen geleistet. Der offizielle Link zu den Dokumenten lautet: www.opengeospatial.org/standards/is.

Aggregation of generic tables describing the Noise Zones, for an infrastructure, the type of infrastructure concerned ROUTE (R), card type C and LD index.

Road infrastructure concerned: A68, C1_albi, C1_castres, D100, D1012, D13, D612, D622, D630, D631, D69, D800, D81, D84, D87, D88, D912, D926, D968, D988, D999A, D999, N112, N126, N88

Limit value exceedance maps (or “type c” maps) maps to be made within the framework of the CBS pursuant to Article 3-II-1°-c of the Decree of 24 March 2006. These are two maps representing the areas where the Lden limit values are exceeded for the year in which the maps are drawn up.

Lden sound level indicator means Level Day-Evening-Night. It corresponds to an equivalent 24-hour sound level in which evening and night noise levels are increased by 5 and 10 dB(A), respectively, to reflect greater discomfort during these periods.

Aggregation obtained by the QGIS MIZOGEO plugin made available by CEREMA.

Data source by infrastructure: CEREMA.

A WMS (Web Map Service) is a service that produces maps of spatially referenced data starting from geographic information, and is a technical specification defined by the OGC (Open Geospatial Consortium). This WMS presents the punctual and area localization of the Commerce Distribution Networks. The maps can be viewed using various software (e.g. QGis)

The European Directive 2002/49/EC on the assessment and management of environmental noise aims at a harmonised assessment of exposure to noise in the Member States. It defines them as representations of data describing a noise situation according to a noise indicator, indicating exceedances of limit values, the number of persons exposed. Noise maps are not prescriptive. These are information documents that are not legally enforceable. As graphic elements, however, they can supplement a Local Planning Plan (LDP). As part of an Urban Travel Plan (UDP), maps can be used to establish baselines and target areas where better traffic management is needed. Carried out pursuant to Article 3-II-1-b of the Decree of 24 March 2006, the map represents the areas where the Lden and Ln boundaries are exceeded.

— LD (Lden) Sound level indicator means Level Day-Evening-Night. — LN (Ln) Sound level indicator for the night period between 22.00 and 06:00.

Aggregation carried out with the QGIS MIZOGEO plugin made available by CEREMA. Data source: CEREMA

This dataset is provided by WMS service (https://geomap.gsmma.gov.tw/mapguide/mapagent/mapagent.fcgi?version1.0.1&formatimage/png). Please add the URL shown in the downloaded file to GIS software (such as QGIS) to select this layer from the directory.

A WMS (Web Map Service) is a service that produces maps of spatially referenced data starting from geographic information, and is a technical specification defined by the OGC (Open Geospatial Consortium). This WMS presents the "Administrative Areas" layer of the Bodies' Reference Territorial Database (BDTRE), structured according to national technical specifications (Prime Minister's Decree of November 10, 2011), collects information referring to the main territorial areas of administrative value. This refers to areas with official value and only the main Administrations are taken into account: Municipality, Mountain Community, Province, Metropolitan City, Region, State. The contents of this service are divided into the following levels corresponding to the BDTRE classes:- Local authority administrative areas The maps can be viewed using various software (e.g. QGis)

We provide WMS/WFS services based on OGC international standards. It can be used in Openlayers Qgis that supports OGC standards. For more information, please refer to the attached file. When called with RestAPI, data is returned in json/xml format.

This dataset contains both large (A0) printable maps of the Torres Strait broken into six overlapping regions, based on a clear sky, clear water composite Sentinel 2 composite imagery and the imagery used to create these maps. These maps show satellite imagery of the region, overlaid with reef and island boundaries and names. Not all features are named, just the more prominent features. This also includes a vector map of Ashmore Reef and Boot Reef in Coral Sea as these were used in the same discussions that these maps were developed for. The map of Ashmore Reef includes the atoll platform, reef boundaries and depth polygons for 5 m and 10 m.

This dataset contains all working files used in the development of these maps. This includes all a copy of all the source datasets and all derived satellite image tiles and QGIS files used to create the maps. This includes cloud free Sentinel 2 composite imagery of the Torres Strait region with alpha blended edges to allow the creation of a smooth high resolution basemap of the region.

The base imagery is similar to the older base imagery dataset: Torres Strait clear sky, clear water Landsat 5 satellite composite (NERP TE 13.1 eAtlas, AIMS, source: NASA).

Most of the imagery in the composite imagery from 2017 - 2021.

Method: The Sentinel 2 basemap was produced by processing imagery from the World_AIMS_Marine-satellite-imagery dataset (not yet published) for the Torres Strait region. The TrueColour imagery for the scenes covering the mapped area were downloaded. Both the reference 1 imagery (R1) and reference 2 imagery (R2) was copied for processing. R1 imagery contains the lowest noise, most cloud free imagery, while R2 contains the next best set of imagery. Both R1 and R2 are typically composite images from multiple dates.

The R2 images were selectively blended using manually created masks with the R1 images. This was done to get the best combination of both images and typically resulted in a reduction in some of the cloud artefacts in the R1 images. The mask creation and previewing of the blending was performed in Photoshop. The created masks were saved in 01-data/R2-R1-masks. To help with the blending of neighbouring images a feathered alpha channel was added to the imagery. The processing of the merging (using the masks) and the creation of the feathered borders on the images was performed using a Python script (src/local/03-merge-R2-R1-images.py) using the Pillow library and GDAL. The neighbouring image blending mask was created by applying a blurring of the original hard image mask. This allowed neighbouring image tiles to merge together.

The imagery and reference datasets (reef boundaries, EEZ) were loaded into QGIS for the creation of the printable maps.

To optimise the matching of the resulting map slight brightness adjustments were applied to each scene tile to match its neighbours. This was done in the setup of each image in QGIS. This adjustment was imperfect as each tile was made from a different combinations of days (to remove clouds) resulting in each scene having a different tonal gradients across the scene then its neighbours. Additionally Sentinel 2 has slight stripes (at 13 degrees off the vertical) due to the swath of each sensor having a slight sensitivity difference. This effect was uncorrected in this imagery.

Single merged composite GeoTiff: The image tiles with alpha blended edges work well in QGIS, but not in ArcGIS Pro. To allow this imagery to be used across tools that don't support the alpha blending we merged and flattened the tiles into a single large GeoTiff with no alpha channel. This was done by rendering the map created in QGIS into a single large image. This was done in multiple steps to make the process manageable.

The rendered map was cut into twenty 1 x 1 degree georeferenced PNG images using the Atlas feature of QGIS. This process baked in the alpha blending across neighbouring Sentinel 2 scenes. The PNG images were then merged back into a large GeoTiff image using GDAL (via QGIS), removing the alpha channel. The brightness of the image was adjusted so that the darkest pixels in the image were 1, saving the value 0 for nodata masking and the boundary was clipped, using a polygon boundary, to trim off the outer feathering. The image was then optimised for performance by using internal tiling and adding overviews. A full breakdown of these steps is provided in the README.md in the 'Browse and download all data files' link.

The merged final image is available in export\TS_AIMS_Torres Strait-Sentinel-2_Composite.tif.

Change Log: 2023-03-02: Eric Lawrey Created a merged version of the satellite imagery, with no alpha blending so that it can be used in ArcGIS Pro. It is now a single large GeoTiff image. The Google Earth Engine source code for the World_AIMS_Marine-satellite-imagery was included to improve the reproducibility and provenance of the dataset, along with a calculation of the distribution of image dates that went into the final composite image. A WMS service for the imagery was also setup and linked to from the metadata. A cross reference to the older Torres Strait clear sky clear water Landsat composite imagery was also added to the record.

22 Nov 2023: Eric Lawrey Added the data and maps for close up of Mer. - 01-data/TS_DNRM_Mer-aerial-imagery/ - preview/Torres-Strait-Mer-Map-Landscape-A0.jpeg - exports/Torres-Strait-Mer-Map-Landscape-A0.pdf Updated 02-Torres-Strait-regional-maps.qgz to include the layout for the new map.

Source datasets: Complete Great Barrier Reef (GBR) Island and Reef Feature boundaries including Torres Strait Version 1b (NESP TWQ 3.13, AIMS, TSRA, GBRMPA), https://eatlas.org.au/data/uuid/d2396b2c-68d4-4f4b-aab0-52f7bc4a81f5

Geoscience Australia (2014b), Seas and Submerged Lands Act 1973 - Australian Maritime Boundaries 2014a - Geodatabase [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, https://dx.doi.org/10.4225/25/5539DFE87D895

Basemap/AU_GA_AMB_2014a/Exclusive_Economic_Zone_AMB2014a_Limit.shp The original data was obtained from GA (Geoscience Australia, 2014a). The Geodatabase was loaded in ArcMap. The Exclusive_Economic_Zone_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

Geoscience Australia (2014a), Treaties - Australian Maritime Boundaries (AMB) 2014a [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, http://dx.doi.org/10.4225/25/5539E01878302 Basemap/AU_GA_Treaties-AMB_2014a/Papua_New_Guinea_TSPZ_AMB2014a_Limit.shp The original data was obtained from GA (Geoscience Australia, 2014b). The Geodatabase was loaded in ArcMap. The Papua_New_Guinea_TSPZ_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

AIMS Coral Sea Features (2022) - DRAFT This is a draft version of this dataset. The region for Ashmore and Boot reef was checked. The attributes in these datasets haven't been cleaned up. Note these files should not be considered finalised and are only suitable for maps around Ashmore Reef. Please source an updated version of this dataset for any other purpose. CS_AIMS_Coral-Sea-Features/CS_Names/Names.shp CS_AIMS_Coral-Sea-Features/CS_Platform_adj/CS_Platform.shp CS_AIMS_Coral-Sea-Features/CS_Reef_Boundaries_adj/CS_Reef_Boundaries.shp CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth5m_Coral-Sea.shp CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth10m_Coral-Sea.shp

Murray Island 20 Sept 2011 15cm SISP aerial imagery, Queensland Spatial Imagery Services Program, Department of Resources, Queensland This is the high resolution imagery used to create the map of Mer.

Marine satellite imagery (Sentinel 2 and Landsat 8) (AIMS), https://eatlas.org.au/data/uuid/5d67aa4d-a983-45d0-8cc1-187596fa9c0c - World_AIMS_Marine-satellite-imagery

Data Location: This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\TS_AIMS_Torres-Strait-Sentinel-2-regional-maps. On the eAtlas server it is stored at eAtlas GeoServer\data\2020-2029-AIMS.

Today, deep neural networks are widely used in many computer vision problems, also for geographic information systems (GIS) data. This type of data is commonly used for urban analyzes and spatial planning. We used orthophotographic images of two residential districts from Kielce, Poland for research including urban sprawl automatic analysis with Transformer-based neural network application.Orthophotomaps were obtained from Kielce GIS portal. Then, the map was manually masked into building and building surroundings classes. Finally, the ortophotomap and corresponding classification mask were simultaneously divided into small tiles. This approach is common in image data preprocessing for machine learning algorithms learning phase. Data contains two original orthophotomaps from Wietrznia and Pod Telegrafem residential districts with corresponding masks and also their tiled version, ready to provide as a training data for machine learning models.Transformed-based neural network has undergone a training process on the Wietrznia dataset, targeted for semantic segmentation of the tiles into buildings and surroundings classes. After that, inference of the models was used to test model's generalization ability on the Pod Telegrafem dataset. The efficiency of the model was satisfying, so it can be used in automatic semantic building segmentation. Then, the process of dividing the images can be reversed and complete classification mask retrieved. This mask can be used for area of the buildings calculations and urban sprawl monitoring, if the research would be repeated for GIS data from wider time horizon.Since the dataset was collected from Kielce GIS portal, as the part of the Polish Main Office of Geodesy and Cartography data resource, it may be used only for non-profit and non-commertial purposes, in private or scientific applications, under the law "Ustawa z dnia 4 lutego 1994 r. o prawie autorskim i prawach pokrewnych (Dz.U. z 2006 r. nr 90 poz 631 z późn. zm.)". There are no other legal or ethical considerations in reuse potential.Data information is presented below.wietrznia_2019.jpg - orthophotomap of Wietrznia districtmodel's - used for training, as an explanatory imagewietrznia_2019.png - classification mask of Wietrznia district - used for model's training, as a target imagewietrznia_2019_validation.jpg - one image from Wietrznia district - used for model's validation during training phasepod_telegrafem_2019.jpg - orthophotomap of Pod Telegrafem district - used for model's evaluation after training phasewietrznia_2019 - folder with wietrznia_2019.jpg (image) and wietrznia_2019.png (annotation) images, divided into 810 tiles (512 x 512 pixels each), tiles with no information were manually removed, so the training data would contain only informative tilestiles presented - used for the model during training (images and annotations for fitting the model to the data)wietrznia_2019_vaidation - folder with wietrznia_2019_validation.jpg image divided into 16 tiles (256 x 256 pixels each) - tiles were presented to the model during training (images for validation model's efficiency); it was not the part of the training datapod_telegrafem_2019 - folder with pod_telegrafem.jpg image divided into 196 tiles (256 x 265 pixels each) - tiles were presented to the model during inference (images for evaluation model's robustness)Dataset was created as described below.Firstly, the orthophotomaps were collected from Kielce Geoportal (https://gis.kielce.eu). Kielce Geoportal offers a .pst recent map from April 2019. It is an orthophotomap with a resolution of 5 x 5 pixels, constructed from a plane flight at 700 meters over ground height, taken with a camera for vertical photos. Downloading was done by WMS in open-source QGIS software (https://www.qgis.org), as a 1:500 scale map, then converted to a 1200 dpi PNG image.Secondly, the map from Wietrznia residential district was manually labelled, also in QGIS, in the same scope, as the orthophotomap. Annotation based on land cover map information was also obtained from Kielce Geoportal. There are two classes - residential building and surrounding. Second map, from Pod Telegrafem district was not annotated, since it was used in the testing phase and imitates situation, where there is no annotation for the new data presented to the model.Next, the images was converted to an RGB JPG images, and the annotation map was converted to 8-bit GRAY PNG image.Finally, Wietrznia data files were tiled to 512 x 512 pixels tiles, in Python PIL library. Tiles with no information or a relatively small amount of information (only white background or mostly white background) were manually removed. So, from the 29113 x 15938 pixels orthophotomap, only 810 tiles with corresponding annotations were left, ready to train the machine learning model for the semantic segmentation task. Pod Telegrafem orthophotomap was tiled with no manual removing, so from the 7168 x 7168 pixels ortophotomap were created 197 tiles with 256 x 256 pixels resolution. There was also image of one residential building, used for model's validation during training phase, it was not the part of the training data, but was a part of Wietrznia residential area. It was 2048 x 2048 pixel ortophotomap, tiled to 16 tiles 256 x 265 pixels each.

Aggregation of generic tables describing the Noise Zones for all terrestrial infrastructure, map type A and Lden index. ‘Type a’ exposure cards: maps to be made within the framework of the CBS pursuant to Article 3-II-1°-a of the Decree of 24 March 2006. This is a dataset representing for the year of mapping: — areas exposed to more than 55 dB(A) in Lden They represent the isophone curves of 5 in 5 dB(A). Lden: sound level indicator means Level Day-Evening-Night. It corresponds to an equivalent 24-hour sound level in which evening and night noise levels are increased by 5 and 10 dB(A), respectively, to reflect greater discomfort during these periods. Aggregation obtained by the QGIS MIZOGEO plugin made available by CEREMA. Data source by infrastructure: CEREMA.

A WMS (Web Map Service) is a service that produces maps of spatially referenced data starting from geographic information, and is a technical specification defined by the OGC (Open Geospatial Consortium). This WMS features the layer "Deep Aquifer Recharge Areas"; the data was produced in implementation of paragraph 4 of article 24 of the Rules of the Water Protection Plan. Approval of the methodology used and of the limitation on a scale of 1:250,000. This limitation was approved with D.D. 268 of 21 July 2016. The data represents the limits towards the plain of the recharging areas in the strict sense, they have been adapted to the basis of the regional BDTRE (2017 edition) in order to facilitate the implementation of the discipline approved by the Regional Council with Resolution no. 12-6441 of 2 February 2018, by the municipalities. A similar examination was conducted on the upstream limit of the recharging areas in the strict sense in areas where there is no buffer strip. Maps in WMS format can be viewed using various software (e.g. QGis)

The 5m DEM is derived from the LiDAR2019B dataset (consisting of the 2018, 2019A and 2019B datasets). The 5m DEM has a vertical accuracy of 30cm. The height reference used is the SA Land Levelling Datum and the SAGEOID2010 was employed.The City of Cape Town Ground Level Map 2019 is defined in the City of Cape Town Municipal Planning Amendment By-law, 2019 as: “‘City of Cape Town Ground Level Map’ means a map approved in terms of the development management scheme, indicating the existing ground level based on floating point raster’s and a contour dataset from LiDAR information available to the City”. The Ground Level Map was approved by the City Council on the 27th July 2023.All Raster Image Services (REST):https://cityimg.capetown.gov.za/erdas-iws/esri/GeoSpatial%20Datasets/rest/services/All Raster Image Services (WMS):Use URL below to add WMS Server Connection in ArcGIS Desktop, ArcPro, QGIS, AutoCAD, etc.https://cityimg.capetown.gov.za/erdas-iws/ogc/wms/GeoSpatial Datasets?service=WMS&request=getcapabilities&For a copy or subset of this dataset, please contact the City Maps Office: city.maps@capetown.gov.zaCCT Ground Level Map: ‘How to Access’ Guide – External Users: CCT Ground Level Map: ‘How to Access’ Guide – External Users | Open Data Portal (arcgis.com)Geomatics Ground Level Map Explainer: Geomatics Ground Level Map Explainer | Open Data Portal (arcgis.com)Land Use Management Ground Level Map Explainer: Land Use Management Ground Level Map Explainer | Open Data Portal (arcgis.com)

A WMS (Web Map Service) is a service that produces maps of spatially referenced data starting from geographic information, and is a technical specification defined by the OGC (Open Geospatial Consortium). This WMS presents the "Hydrography" layer of the Bodies' Territorial Reference Database (BDTRE), structured according to national technical specifications (DPCM November 10, 2011), collects the topics related to the description of water bodies, the coast and marine waters. The contents of this service are divided into the following levels corresponding to the BDTRE classes: - Internal and transitional waters - Marine waters - Perennial glaciers and snowfields - Hydrographic network The maps can be viewed using various software (e.g. QGis)

A WMS (Web Map Service) is a service that produces maps of spatially referenced data starting from geographic information, and is a technical specification defined by the OGC (Open Geospatial Consortium). This WMS presents the "Real estates and anthropizations" layer of the Reference Territorial Database of Bodies (BDTRE), structured according to the national technical specifications (DPCM November 10, 2011), collects the definition of all those objects that derive from anthropic activity in the territory and that do not constitute transport infrastructure (described instead in the specific layer). The contents of this service are divided into the following levels corresponding to the classes of the BDTRE:- buildings (both of a residential and industrial nature and tertiary activity);- artefacts (works that do not have a stable character in terms of habitability and human location); variously located throughout the territory; - transport works (works such as artefacts but of greater complexity); - soil defense works; - hydraulic defense and hydraulic control works. The maps can be viewed using various software (e.g. QGis)

This dataset is provided through WMS service (https://geomap.gsmma.gov.tw/mapguide/mapagent/mapagent.fcgi?version1.0.0&formatimage/png). Please add the URL shown in the downloaded file to GIS software (such as QGIS), and you can select this layer in the directory.

A WMS (Web Map Service) is a service that produces maps of spatially referenced data starting from geographic information, and is a technical specification defined by the OGC (Open Geospatial Consortium). This WMS presents the "Orography" layer of the Bodies' Reference Territorial Data Bank (BDTRE), structured according to the national technical specifications (DPCM 10 November 2011), groups the topic of altimetry with the description of contour lines and elevations, the theme of bathymetry with the description of the bathymetric curves and bathymetric points (seabeds), the theme of the natural forms of the land, i.e. those auxiliary elements for reading the morphology of the territory and the theme of digital terrain models. The contents of this service are divided into the following levels corresponding to the BDTRE classes: - Altimetry - Bathymetry - Landforms - transport works (works such as artefacts but of greater complexity); - soil protection works; - hydraulic works defense and hydraulic regulation. The maps can be viewed using various software (e.g. QGis)

Aggregation of generic tables describing Noise Zones, for an infrastructure, type of infrastructure concerned ROUTE (R), type C map according to indicator Ln (night).

This map is a graphic representation of areas where the sound level in Lden exceeds the limit value of 62 dB.

Map to be made as part of the CBS in accordance with Articles R.571-37 and R.571-38 of the Environmental Code.

Aggregation obtained by the QGIS MIZOGEO plugin made available by CEREMA.