This dataset contains information, opening times and locations related to Brisbane City Council golf courses.More information about these golf courses can be found on the Brisbane City Council website.

This deep learning model is used to extract building footprints from high-resolution (10–40 cm) imagery. Building footprint layers are useful in preparing base maps and analysis workflows for urban planning and development, insurance, taxation, change detection, infrastructure planning, and a variety of other applications.Digitizing building footprints from imagery is a time-consuming task and is commonly done by digitizing features manually. Deep learning models have a high capacity to learn these complex workflow semantics and can produce superior results. Use this deep learning model to automate this process and reduce the time and effort required for acquiring building footprints.Using 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.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.Input8-bit, 3-band high-resolution (10–40 cm) imagery. Note: Imagery has to be analyzed at 30 cm resolution for best results.OutputFeature class containing building footprints.Applicable geographiesThe model is expected to work in Australia.Model architectureThe model uses the MaskRCNN model architecture implemented using ArcGIS API for Python.Accuracy metricsThe model has an average precision score of 79.4 percent.Training dataThis model has been trained on an Esri proprietary building footprint extraction dataset.Limitations • False positives are observed near the costal areas. These can be filtered out using the confidence values. • A random shift between footprints and imagery (around 3-7 meter) has been observed in some areas. • The model does not work well with highly oblique (off nadir) imagery, especially when delineating footprints of high rise buildings.Sample resultsHere are a few results from the model. To view more, see this story.

This Deep Learning Package (DLPK) acts as a bridge between ArcGIS Pro and vision language models from OpenAI and Meta. Vision-language models are renowned for their advanced capabilities in natural language processing and understanding, as well as their ability to interpret and generate human-like text. The integration of these models into a DLPK enhances their utility by enabling them to process images and perform zero-shot classification of objects in imagery.Use this deep learning package to leverage the power of large vision language models to perform object classification on images and rasters within ArcGIS Pro. This DLPK allows for flexibility in classifying objects, as it is not restricted to predefined classes; users can specify custom class labels at the time of running the tool. This capability opens up new avenues for analysis and interpretation of spatial data, making it easier for professionals in fields such as environmental science, urban planning, and remote sensing to extract meaningful insights from imagery. These models can support disaster response and recovery efforts.Note: This model requires internet connection to work. The data used for classification, including the imagery and possible class labels, will be shared with OpenAI when using the GPT models. However, if you are using the Llama Vision model, it operates locally and does not require an internet connection, ensuring that your data remains on your machine without being shared externally. This model is not supported in ArcGIS Online.Using the modelFollow the guide to use the model. Before using the Llama vision model, ensure that the supported deep learning libraries are installed. For more details, check the Deep Learning Libraries Installer for ArcGIS. OpenAI models do not require deep learning libraries to be installed.Fine-tuning the modelThis model cannot be fine-tuned using ArcGIS.Input8-bit RGB imagery.OutputFeature class with classification of features in the imagery.Applicable geographiesThis model is expected to work well globally.Model architectureThe implementation uses OpenAI's vision language models or Llama Vision models.Sample resultsHere are a few sample results from the model.

This feature layer provides access to OpenStreetMap (OSM) highways data for Australia and Oceania, which is updated every 5 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM line (way) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes highway features defined as a query against the hosted feature layer (i.e. highway is not blank).In OSM, a highway describes any kind of motorway, road, street or path. These features are identified with a highway tag. There are hundreds of different tag values for highway used in the OSM database. In this feature layer, unique symbols are used for several of the most popular highway types, while lesser used types are grouped in an "other" category.Zoom in to large scales (e.g. Streets level or 1:20k scale) to see the highway features display. You can click on a feature to get the name of the highway (if available). The name of the highway will display by default at large scales (e.g. Street level of 1:5k scale). Labels can be turned off in your map if you prefer.Create New LayerIf you would like to create a more focused version of this highway layer displaying just one or two highway types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. highway is path), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers (e.g. cycleway and pedestrian) that are ready to use, but not for every type of highway.Important Note: if you do create a new layer, it should be provided under the same Terms of Use and include the same Credits as this layer. You can copy and paste the Terms of Use and Credits info below in the new Item page as needed.