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 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

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