Revolutionizing GIS with Deep Learning and ArcGIS

The intersection of Artificial Intelligence (AI) and Geographic Information Systems (GIS) is unlocking unprecedented opportunities. Deep learning, a subset of AI, is rapidly advancing, achieving and even surpassing human accuracy in tasks like image recognition and text translation. This synergy is transforming how we understand and interact with our world, enhancing capabilities in precision agriculture, crime pattern analysis, and predictive disaster management. This article explores how deep learning is reshaping GIS, particularly within the ArcGIS platform.

Machine learning has long been integral to spatial analysis within GIS. Tools and algorithms are applied to geoprocessing for classification, clustering, and prediction. For instance, vector machine algorithms create land-cover classifications, and geographically weighted regression models spatially varying relationships. However, these methods often require expert input to identify factors influencing the outcome. Deep learning, inspired by the human brain, automates feature identification directly from data, offering a significant advancement over traditional machine learning techniques. Deep learning uses computer-generated neural networks to solve problems and make predictions.

The rise of deep learning is fueled by three primary factors: the availability of vast datasets, increased computing power, and algorithmic improvements. The Internet, sensors, and satellites generate massive amounts of data. Cloud computing and powerful GPUs, driven by the gaming industry, provide the necessary computational resources. Algorithmic advancements have also enabled more effective training of deep neural networks.

Computer vision, the ability of computers to 'see,' is a key area where deep learning excels. This is invaluable for GIS, given the immense volume of satellite, aerial, and drone imagery. Deep learning facilitates tasks like image classification (categorizing geotagged photos), object detection (locating objects in imagery for infrastructure mapping), and semantic segmentation (classifying each image pixel for land-cover analysis). For example, deep learning can detect swimming pools in residential areas or classify land cover with high precision. Instance segmentation, a more precise form of object detection, can improve basemaps by adding building footprints or reconstructing 3D buildings from LiDAR data. Esri's collaboration with NVIDIA to automate 3D building model creation for Miami-Dade County exemplifies this capability.

Deep learning significantly enhances digital map creation by automating the extraction of road networks and building footprints from satellite imagery. Imagine applying a trained deep learning model to a large area and generating a map with all the roads, enabling the creation of driving directions. This is especially useful in developing countries or rapidly developing areas. Instance segmentation models, like Mask R-CNN, facilitate building footprint segmentation without manual digitizing. Tools like the Regularize Building Footprint tool in ArcGIS Pro can then refine these footprints for accuracy.

ArcGIS provides comprehensive tools for every stage of the data science workflow, from data preparation to model training and spatial analysis. Users can leverage content from Esri’s ArcGIS Living Atlas of the World to enrich their analysis. ArcGIS Pro includes tools for data preparation and deploying trained models. ArcGIS Image Server in ArcGIS Enterprise 10.7 offers the ability to deploy deep learning models at scale. The arcgis.learn module in ArcGIS API for Python simplifies deep learning model training. ArcGIS Notebooks provides a ready-to-use environment, and ArcGIS includes built-in Python raster functions for object detection and classification using various deep learning libraries. Python, with libraries like TensorFlow and PyTorch, is the primary language for deep learning, making ArcGIS API for Python and ArcPy natural fits for integration.

Beyond imagery, deep learning is applicable to structured data, such as sensor observations, for tasks like predicting accident probabilities and sales forecasting. Esri is heavily investing in these technologies, including establishing a new R&D center in New Delhi focused on AI and deep learning on satellite imagery and location data. This center aims to advance data science, deep learning, and geospatial AI solutions within the ArcGIS platform, driving future innovations in the field.