An ArcMap map package is a portable file that contains a map document (.mxd) as well as the data referenced by the map layers. (ArcGIS Pro map packages are similar but have different file extensions.) Even if you're not an ArcMap user, you may need at some point to bring a map document or map package into ArcGIS Pro. You don't need ArcMap software to do this tutorial.Estimated time: 20 minutesSoftware requirements: ArcGIS Pro

This deep learning model is used to detect trees in low-resolution drone or aerial imagery. Tree detection can be used for applications such as vegetation management, forestry, urban planning, etc. High resolution aerial and drone imagery can be used for tree detection due to its high spatio-temporal coverage.

This deep learning model is based on MaskRCNN and has been trained on data from the DM Dataset preprocessed and collected by the IST Team.

There is no need of high-resolution imagery you can perform all your analysis on low resolution imagery by detecting the trees with the accuracy of 75% and finetune the model to increase your performance and train on your own data.

Licensing requirements ArcGIS Desktop – ArcGIS Image Analyst and ArcGIS 3D Analyst extensions for ArcGIS Pro ArcGIS Enterprise – ArcGIS Image Server with raster analytics configured ArcGIS Online – ArcGIS Image for ArcGIS Online

Using the model Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.

Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.

Input 3-band low-resolution (70 cm) satellite imagery.

Output Feature class containing detected trees

Applicable geographies The model is expected to work well in the U.A.E.

Model architecture This model is based upon the MaskRCNN python package and uses the Resnet-152 model architecture implemented in pytorch.

Training data This model has been trained on the Satellite Imagery created and Labelled by the team and validated on the different locations with more diverse locations.

Accuracy metrics This model has an average precision score of 0.45.

Sample results Here are a few results from the model.

This stormwater forecast script tool was developed by the Natural Resources Department at the Atlanta Regional Commission.WHAT IS THE STORMWATER FORECAST?In 2022, the District developed a novel water quantity-based indicator, the Stormwater Forecast, to support watershed managers with ongoing challenges related to water quality, streambank erosion, and nuisance flooding.The Stormwater Forecast is a planning-level estimate of the total potential storage volume required by Stormwater Control Measures to manage runoff from development at a basin scale under both current and future conditions. Based on current development patterns, the results of the Stormwater Forecast show the 15-county Metro Atlanta region should be managing up to 27 billion cubic feet of runoff volume with Stormwater Control Measures, and if regulations remain the same the total volumes are estimated to increase by up to 100 percent by 2040. STORMWATER FORECAST USER GUIDEThe Stormwater Forecast User Guide outlines steps for calculating stormwater runoff volumes for an area of interest using the Stormwater Forecast and performing a Stormwater Forecast Gap Analysis using the custom stormwater runoff volume results.STORMWATER FORECAST GEOPROCESSING PACKAGEThe Stormwater Forecast Geoprocessing Package contains the Stormwater Forecast Script Tool and a geodatabase with the following four parameters needed to execute the tool. AreaofInterestStormwaterForecastDevelopedAreaNLCD_Imperviousness_2019.tifThe Stormwater Forecast Script Tool provides users with an automated calculation method for calculating custom stormwater runoff volumes within an area of interest using the Stormwater Forecast.FIELD ABBREVIATIONS AND DESCRIPTIONS FOR STORMWATER FORECAST RESULTSUnique_ID = Unique Identification Characters for Stormwater Forecast SubcatchmentNHD_Sub_ID = National Hydrography Dataset Subcatchment Identification Numbers HUC_12 = Hydrologic Unit Code-12 Identification Numbers County = County Name HUC_8 = Hydrologic Unit Code-8 Identification Numbers MRB = HUC-8 Major River Basin Name Area_Dev_a = 2019 Developed Area, in acresImpv_Area = 2019 Total Impervious Area within Developed Area, in acresAOI_19_WQ = 2019 Water Quality Volume for Area of Interest, in cubic feet AOI_19_CP = 2019 Channel Protection Volume for Area of Interest, in cubic feetAOI_19_OF = 2019 Overbank Flood Protection Volume for Area of Interest, in cubic feetAOI_30_WQ = 2030 Water Quality Volume for Area of Interest, in cubic feet AOI_30_CP = 2030 Channel Protection Volume for Area of Interest, in cubic feetAOI_30_OF = 2030 Overbank Flood Protection Volume for Area of Interest, in cubic feetAOI_40_WQ = 2040 Water Quality Volume for Area of Interest, in cubic feet AOI_40_CP = 2040 Channel Protection Volume for Area of Interest, in cubic feetAOI_40_OF = 2040 Overbank Flood Protection Volume for Area of Interest, in cubic feetRequired Software: Esri’s ArcGIS Pro and Esri’s Spatial Analyst and Image Analyst Extensions

This New Zealand car detection Deep Learning Package will detect cars from high resolution imagery. This model is re-trained from the Esri Car Detection - USA Deep Learning Package and is trained to work better within the New Zealand geography.The model precision had also improved from 0.81 to 0.89. The package is trained to be more aggressive in terms of car detecting and is able to detect most cars that are fully covered in shade or partially blocked by tree canopy. This deep learning model is used to detect cars in high resolution drone or aerial imagery. Car detection can be used for applications such as traffic management and analysis, parking lot utilization, urban planning, etc. It can also be used as a proxy for deriving economic indicators and estimating retail sales. High resolution aerial and drone imagery can be used for car detection due to its high spatio-temporal coverage.Licensing requirementsArcGIS Desktop – ArcGIS Image Analyst and ArcGIS 3D Analyst extensions for ArcGIS ProArcGIS Online – ArcGIS Image for ArcGIS OnlineUsing the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputHigh resolution RGB imagery (7.5 centimetre spatial resolution)OutputFeature class containing detected carsApplicable geographiesThe model is expected to work well with the New Zealand localised data.Model architectureThis model uses the MaskRCNN model architecture implemented in ArcGIS Pro Arcpy.Accuracy metricsThis model has an average precision score of 0.89.Sample resultsHere are a few results from the model.(Post processing are recommended to filter out False Positive Object.e.g (confidence >= x | 0.95) |& ((shape_area/shape_length) >= x | 0.5) |& (class == Car) |& Regularize(feature)3% of detected object will need to be filtered out averagely .To learn how to use this model, see this story

The Atlanta Regional Commission's Stormwater Forecast Tool allows one to forecast the amount of flood waters they can expect given the current rate of land development with impervious surfaces over the next 20 years. For more information about the ARC's Stormwater Forecast Tool please continue reading their description below: This stormwater forecast script tool was developed by the Natural Resources Department at the Atlanta Regional Commission.WHAT IS THE STORMWATER FORECAST?In 2022, the District developed a novel water quantity-based indicator, the Stormwater Forecast, to support watershed managers with ongoing challenges related to water quality, streambank erosion, and nuisance flooding.The Stormwater Forecast is a planning-level estimate of the total potential storage volume required by Stormwater Control Measures to manage runoff from development at a basin scale under both current and future conditions. Based on current development patterns, the results of the Stormwater Forecast show the 15-county Metro Atlanta region should be managing up to 27 billion cubic feet of runoff volume with Stormwater Control Measures, and if regulations remain the same the total volumes are estimated to increase by up to 100 percent by 2040. STORMWATER FORECAST USER GUIDEThe Stormwater Forecast User Guide outlines steps for calculating stormwater runoff volumes for an area of interest using the Stormwater Forecast and performing a Stormwater Forecast Gap Analysis using the custom stormwater runoff volume results.STORMWATER FORECAST GEOPROCESSING PACKAGEThe Stormwater Forecast Geoprocessing Package contains the Stormwater Forecast Script Tool and a geodatabase with the following four parameters needed to execute the tool. AreaofInterestStormwaterForecastDevelopedAreaNLCD_Imperviousness_2019.tifThe Stormwater Forecast Script Tool provides users with an automated calculation method for calculating custom stormwater runoff volumes within an area of interest using the Stormwater Forecast.FIELD ABBREVIATIONS AND DESCRIPTIONS FOR STORMWATER FORECAST RESULTSUnique_ID = Unique Identification Characters for Stormwater Forecast SubcatchmentNHD_Sub_ID = National Hydrography Dataset Subcatchment Identification Numbers HUC_12 = Hydrologic Unit Code-12 Identification Numbers County = County Name HUC_8 = Hydrologic Unit Code-8 Identification Numbers MRB = HUC-8 Major River Basin Name Area_Dev_a = 2019 Developed Area, in acresImpv_Area = 2019 Total Impervious Area within Developed Area, in acresAOI_19_WQ = 2019 Water Quality Volume for Area of Interest, in cubic feet AOI_19_CP = 2019 Channel Protection Volume for Area of Interest, in cubic feetAOI_19_OF = 2019 Overbank Flood Protection Volume for Area of Interest, in cubic feetAOI_30_WQ = 2030 Water Quality Volume for Area of Interest, in cubic feet AOI_30_CP = 2030 Channel Protection Volume for Area of Interest, in cubic feetAOI_30_OF = 2030 Overbank Flood Protection Volume for Area of Interest, in cubic feetAOI_40_WQ = 2040 Water Quality Volume for Area of Interest, in cubic feet AOI_40_CP = 2040 Channel Protection Volume for Area of Interest, in cubic feetAOI_40_OF = 2040 Overbank Flood Protection Volume for Area of Interest, in cubic feetRequired Software: Esri’s ArcGIS Pro and Esri’s Spatial Analyst and Image Analyst Extensions

The Nigeria Geospatial Analytics Market is booming, projected to reach $132.83 million by 2033 with a 5.94% CAGR. Discover key drivers, trends, and leading companies shaping this dynamic sector. Learn about the opportunities in agriculture, government, and more. Recent developments include: April 2023: Abuduganiyu Adebomehin, the Surveyor General of the Federation (SGoF), has praised Sambus Geospatial Nigeria Limited, a provider of solutions, for its dedication to the promotion of a producing high-quality, accurate, and real-time geographic data for Nigeria. The Office of the Surveyor General of the Federation (OSGoF) donated five copies of mapping software (ESRI Arc GIS Pro Advance with ten extensions), which the SGoF accepted in exchange for the praise.. Key drivers for this market are: Commercialization of spatial data, Increased smart city & infrastructure projects. Potential restraints include: Commercialization of spatial data, Increased smart city & infrastructure projects. Notable trends are: Commercialization of spatial data would drive the market in Nigeria.