Data Preparation using ArcGIS Pro and ArcGIS API for Python 5. The trained model classifies NAIP image to six land cover classes: 1) buildings, 2) roads or parking lots, 3) water, 4) harvested, open land or bare land, 5) forest and 6) planted or dark cropland. Using Deep Learning for Feature Extraction and Classification For a human, it's relatively easy to understand what's in an image—it's simple to find an object, like a car or a face; to classify a structure as damaged or undamaged; or to visually identify different land cover types. Updates the Target field in the attribute table to compare reference points to the classified image. Additionally, you can write your own python raster function that use your deep learning library of choice or specific deep learning model/architecture. It was not surprising that the model performed better for frequently seen classes compared to rare classes. The new U-Net model has 1,941,351 parameters (Python #3). ArcGIS Image Server allows you to use statistical or machine learning classification methods to classify remote sensing imagery. While the examples above have focused on imagery and computer vision, deep learning can also be used equally well for processing large volumes of structured data such as observations from sensors, or attributes from a feature layer. Since NAIP image has a large footprint (~7.5km × ~6.5km) and they cannot be fed directly to the model, I converted the NAIP images and corresponding labelled data in a raster format to smaller images. If more than 50% of the chip had background class (0 value), they are dropped from further analysis. So what’s changed now, and made them so successful at what they do? I used Reclassify function in ArcGIS Pro [Ref 7] to convert No-Data to zero and keep the values of other land classes same. See this handy guide to get started! The intersection of artificial intelligence (AI) and GIS is creating massive opportunities that weren’t possible before. Land Use / Land Cover mapping with Machine Learning and Remote Sensing Data in ArcGIS. we collected 12 NAIP images [Ref 1], 8 NAIP images in the north and 4 NAIP images in the south, using ArcGIS Online [Ref 2]. ArcGIS Pro desktop provides a rich environment to process and exploit imagery. Deploy Model in ArcGIS Pro and Run The Trained Model on NAIP Images. Machine Learning in ArcGIS Lauren Bennett, PhD Marjean Pobuda. First, I used the Create Mosaic Dataset function in ArcGIS Pro [Ref 3] to create an empty mosaic dataset in a geodatabase. ArcGIS Pro includes tools for helping with data preparation for deep learning workflows and has being enhanced for deploying trained models for feature extraction or classification. Algorithmic improvements: Finally, researchers have now cracked some of the most challenging aspects of training the deep neural networks through algorithmic improvements and network architectures. ArcGIS has tools to help with every step of the data science workflow: from data preparation and exploratory data analysis, to training the model and to performing spatial analysis and finally disseminating results using web layers and maps and driving field activity. ArcGIS API for Python and ArcPy are a natural fit for integrating with these deep learning libraries and allow your capabilities to expand. This enables you to prepare geospatial data (raster or vector data) faster by leveraging Geoprocessing Tools in ArcGIS Pro that can be used in Python and visualize progress in ArcGIS Pro (Figure 2). The following table lists the available segmentation and classification tools and provides a brief description of each. ArcGIS Pro Image Segmentation, Classification, and Machine Learning. Generates an Esri classifier definition (.ecd) file using the Support Vector Machine (SVM) classification definition. OBIA This is following the ArcGIS Pro … Step I –Image Segmentation •Input a 3 band, 8-bit image •Use raster function to test segmentation parameters-WYSIWYG-Use the bands that discriminate your features of interest best •Apply segmentation parameters in ArcGIS Pro The “Export Training Data for Deep Learning” in ArcGIS Pro 2.4 ver. I want to say thanks to Daniel Wilson and Mary Wahl (Microsoft) who answered my questions during the project. This notebook showcases an end-to-end to land cover classification workflow using ArcGIS API for Python. The .emd file is a JSON file that describes the trained deep learning model. Learn the latest GIS technology through free live training seminars, self-paced courses, or classes taught by Esri experts. Tools of the Segmentation and Classification toolset Conclusion: Although it is a relatively small dataset, Mask-RCNN, and Deeplabv3+ deep learning models both present promising results for super-high-resolution image segmentation using transferred learning techniques. In other word, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. Deploy Model in ArcGIS Pro and Run The Trained Model on NAIP Images 8. "FINAL OBIA Classification_201903250734480982902" is the object-based segmentation and classification of the Raleigh_LC08_20160413.tif file. After creating the .emd file (Figure 8), I ran inference on 12 NAIP images (Figure 9). Machine learning is one type of engine that makes this possible. 1] https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery2] https://www.arcgis.com/index.html3] http://pro.arcgis.com/en/pro-app/tool-reference/data-management/create-mosaic-dataset.htm4] http://pro.arcgis.com/en/pro-app/tool-reference/data-management/add-rasters-to-mosaic-dataset.htm5] https://pro.arcgis.com/en/pro-app/help/analysis/image-analyst/training-samples-manager.htm6] http://pro.arcgis.com/en/pro-app/tool-reference/conversion/feature-to-raster.htm7] http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/reclassify.htm8] http://pro.arcgis.com/en/pro-app/tool-reference/image-analyst/export-training-data-for-deep-learning.htm9] http://pro.arcgis.com/en/pro-app/help/analysis/image-analyst/deep-learning-in-arcgis-pro.htm10] https://github.com/ArcGIS/geoai-cookiecutter, https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery, http://pro.arcgis.com/en/pro-app/tool-reference/data-management/create-mosaic-dataset.htm, http://pro.arcgis.com/en/pro-app/tool-reference/data-management/add-rasters-to-mosaic-dataset.htm, https://pro.arcgis.com/en/pro-app/help/analysis/image-analyst/training-samples-manager.htm, http://pro.arcgis.com/en/pro-app/tool-reference/conversion/feature-to-raster.htm, http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/reclassify.htm, http://pro.arcgis.com/en/pro-app/tool-reference/image-analyst/export-training-data-for-deep-learning.htm, http://pro.arcgis.com/en/pro-app/help/analysis/image-analyst/deep-learning-in-arcgis-pro.htm, https://github.com/ArcGIS/geoai-cookiecutter, Detecting and Counting Objects with OpenCV, Using Sktime for Time Series Classification(part 2), Prometheus Metrics for Batch Jobs on Kubernetes, Behind the Working of Music Search Apps Like Shazam: Create Your Own Music Search App, Every ML Engineer Needs to Know Neural Network Interpretability. Land Use / Land Cover mapping with Machine Learning and Remote Sensing Data in ArcGIS. For instance, in the image in Fig 1, the cat is in the yellow pixels, and the green pixels belong to the ground class and the sky is in blue. I defined the mean of intersection over Union (IoU) to calculate the accuracy of the model using validation data in the training run. Beside land cover classification approaches that you might be able to find in other blogs, I am also interested to show here that how you can leverage ArcGIS API for Python and ArcGIS Pro and integrate them with deep learning tools (e.g., Keras here). First, you'll import the Python libraries that you'll use to build a predictive model and perform machine learning. ArcGIS Image Server in the ArcGIS Enterprise 10.7 release has similar capabilities and allow deploying deep learning models at scale by leveraging distributed computing. One area of AI where deep learning has done exceedingly well is computer vision, or the ability for computers to see. Image segmentation is one of the critical problems in the field of computer vision. The recent success of AI brings new opportunity to this field. Segmentation groups pixels in close proximity and having similar spectral characteristics into a segment, which doesn't need any training data and is considered as unsupervised learning . Data Augmentation 6. Now that machine learning algorithms are available for everyone, they can be used to solve spatial problems. I chose the chip size of 256 and stride size 64 along X and Y axes. Generates an Esri classifier definition (.ecd) file using the Support Vector Machine (SVM) classification definition. Using Deep Learning for Feature Extraction and Classification For a human, it's relatively easy to understand what's in an image—it's simple to find an object, like a car or a face; to classify a structure as damaged or undamaged; or to visually identify different land cover types. The importance of image segmentation has been illustrated in various applications such as self-driving vehicles, human-computer interaction, virtual reality etc. Sharing a Data Science project with other data scientists is always challenging since everybody has their own structure to build a template for a Data Science project. While low resolution, spatial (e.g., 30m) and temporal (e.g., every 5 years), land cover maps have been developed by various agencies (e.g., USGS, USDA, NASA) for the entire Europe and USA, creation of real-time high resolution spatial and temporal land cover maps (e.g., 1 meter) at a regional scale is lacking. The total number of chips for each NAIP image depends on labeled data across each NAIP image. Machine learning is one type of engine that makes this possible, ... also referred to as image segmentation, ... (object detection and pixel classification) in ArcGIS Pro and Image Server. Wouldn’t it be great if the machine figured out what those factors/features should be just by looking at the data? The figure below shows some of the most important computer vision tasks or use cases and how they can be applied to GIS: The simplest is Image Classification, in which the computer assigns a label, such as ‘cat’ or ‘dog’ to an image. Unsupervised pixel classification in ArcGIS Pro 2.4.0: ISO cluster will not let me classify image into three classes I am using ArcGIS Pro 2.4.0, and am attempting to classify a field into a schema with three categories: Leaves, Shadows, and Soil. ArcGIS includes tools such as Regularize Building Footprints just for this. From the training data, I generated 420, 438, 702, 1008, 837, 891 chips for buildings, roads, water, harvested lands, forest, and planted lands, respectively. It contains model definition parameters that are required to run the inference tools, and it should be modified by the data scientist that trained the model. Deep-Learning methods tend to perform well with high amounts of data as compared to machine learning methods which is one of the drawbacks of these models. Updates the Target field in the attribute table in order to be able to compare reference points to the classified image. Deep Learning—Perform image feature recognition using deep learning techniques. Integration of external deep learning model frameworks currently works any deep learning framework, provided you can supply a raster function. Extracting information from remotely sensed imagery is an important step to providing timely information for your GIS. 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