{"id":1071621,"date":"2023-11-15T18:05:31","date_gmt":"2023-11-16T02:05:31","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=1071621"},"modified":"2023-11-15T09:05:39","modified_gmt":"2023-11-15T17:05:39","slug":"deep-learning-with-arcgis-pro-tips-tricks","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/imagery\/deep-learning-with-arcgis-pro-tips-tricks","title":{"rendered":"Deep Learning with ArcGIS Pro Tips & Tricks: Part 1"},"author":19291,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[22931,22941],"tags":[42181,186132,757081,665211,115262],"industry":[],"product":[36561],"class_list":["post-1071621","blog","type-blog","status-publish","format-standard","hentry","category-imagery","category-mapping","tag-arcgis-pro","tag-deep-learning","tag-deep-learning-models","tag-geoai","tag-imagery","product-arcgis-pro"],"acf":{"short_description":"Prepare your environment to run out-of-the-box deep learning geoprocessing tools in ArcGIS Pro.","flexible_content":[{"acf_fc_layout":"content","content":"

Satellite imagery can contain a wealth of information, from the number of buildings in a city to the type of crops being grown in fields across the world. But extracting this data from an image is more complicated than working with vector datasets. Historically, to extract the buildings, or swimming pools, or palm trees in an image you would have needed to manually digitize each feature, a process that could take weeks or years depending on the size of the image. But with improvements in computing power and new, accessible tools for deep learning in ArcGIS Pro, anyone can train a computer to do the work of identifying and extracting features from imagery.<\/p>\n

At the highest level, deep learning, which is a type of machine learning, is a process where the user creates training samples, for example by drawing polygons over rooftops, and the computer model learns from these training samples and scans the rest of the image to identify similar features. This blog post will be the first in a three-part series diving deeper into the process, starting with the software and hardware requirements to run a deep learning model in ArcGIS Pro.<\/p>\n

Deep learning prerequisites<\/h1>\n

Deep learning is a type of machine learning that relies on multiple layers of nonlinear processing for feature identification and pattern recognition, described in a model. Deep learning models can be integrated with ArcGIS Pro for object detection, object classification, and image classification.<\/p>\n

There will be many acronyms used in this blog, so we have defined them here for your reference:<\/p>\n

GPU: graphics processing unit<\/em><\/p>\n

CPU: central processing unit<\/em><\/p>\n

RAM: random access memory<\/em><\/p>\n

CUDA: Compute Unified Device Architecture<\/em><\/p>\n

DLPK: ArcGIS Pro deep learning model package<\/em><\/p>\n

Inferencing is the process in which information learned during the deep learning training process is put to work detecting similar features in the datasets. ArcGIS Pro uses an external third-party framework and model definition file to run the inference geoprocessing tools. Model definition files and (.dlpk) packages can be used multiple times as inputs to the geoprocessing tools, allowing you to assess multiple images over different locations and time periods using the same trained model.<\/p>\n

Pretrained deep learning packages (dlpks) are becoming more readily available as the trend of deploying deep learning workflows shifts from complex Python scripts to out-of-the-box tools. In this blog series, we will cover the prerequisite steps and some tips and tricks to run a seamless deep learning inferencing process. We will also dive into configuring the key parameters for those who are interested in understanding best practices in deploying deep learning tools.<\/p>\n

This blog series will cover a step-by-step workflow that will help you run a deep learning exercise in ArcGIS Pro. We will also go over some essential checkpoints to ensure that you can run the tools without facing any unexpected errors.<\/p>\n

Before we can train or run any models, we need to ensure that your environment is set up to perform deep learning. This post will walk you through the following checklist:<\/p>\n