https://digitalsloan.com/project-blog daily 0.75 2025-04-07 https://digitalsloan.com/project-blog/gis-with-deckgl-react monthly 0.5 2025-04-07 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/df2a190e-fe01-4ef9-8eb1-9e3a639f10a6/24120600_gis_uca1.png Project Blog - GIS with DeckGL + React - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/81f8f1f4-cf93-44a4-9737-3aa233f7b02a/25040600_EO_shpfileDesc-3-01.jpg Project Blog - GIS with DeckGL + React - Make it stand out CRS from the shapefile .prj file matched with EPSG references https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/14f6b792-4adf-4d34-9c60-6282650a395f/25040600_EO_shpfileDesc-5-01.jpg Project Blog - GIS with DeckGL + React - Make it stand out Using the EPSG codes with Pyproj for coordinate transformations. This process is executed by a custom Python script for shapefile to geojson conversions. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/f10d6ad8-a1bf-4d2c-9041-8c15100284cd/Picture1.png Project Blog - GIS with DeckGL + React - Make it stand out Masked pixels from urban materials geotiff. The white blobs are areas where pixels were classified as being urban materials such as concrete and asphalt. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1743362212683-JVX1Q4L02ZVDIQG221VZ/24120600_gis_ucaPolgonsFilled.png Project Blog - GIS with DeckGL + React https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1743362189889-PCHBHCYGHP0SMHZ9POKT/24120600_gis_uca2018-19.png Project Blog - GIS with DeckGL + React https://digitalsloan.com/project-blog/eo-interface-development-update-deckgl-layers monthly 0.5 2025-01-20 https://digitalsloan.com/project-blog/eo-pt1 monthly 0.5 2024-10-06 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/64d930ef-f4d6-4890-b0b0-ce91531d2719/24071400_EO_DOC_dataAquisition.jpg Project Blog - EO - Pt.1 - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/0160b9e6-5e3b-43a2-917f-ec2460c9754a/24093000_EO_tilesIso.png Project Blog - EO - Pt.1 - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/7b8ce3ab-9516-451f-9e59-9550f34b4e82/24093000_EO_logJson.png Project Blog - EO - Pt.1 - Make it stand out During the tiling process, the geographic coordinates of the tile bounding box are logged to a log.json file which is referenced in later processes. The log.json file also tracks processing times for each tile. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/05cff27e-41ed-40d9-b9d1-f8b40554f786/24100600_EO_tileOverlap.png Project Blog - EO - Pt.1 - Make it stand out NOTE: While this is the current process of determining if a point from Landsat tiles are within 3DEP tile bounds, this process is being updated for better efficiency. I will be updating the data storage schema for efficient union and overlap filtering. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/ef853faf-fe48-4033-84d6-c1fbe9ef15e8/24071401_EO_DOC_bandCombinations.jpg Project Blog - EO - Pt.1 - Make it stand out Band combinations for deriving spectral indices https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/e344a4ae-2502-4ffa-aa0e-81baba2f1944/24072002_EO_DOC_analysisProcess_V2.jpg Project Blog - EO - Pt.1 - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/0be9eaa9-c787-4251-9da8-459635ee1e25/24093001_EO_lst%26Ndvi.png Project Blog - EO - Pt.1 - Make it stand out Examples of mean LST & NDVI and rate of change of LST and NDVI. Example location: Dallas Texas https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/0e7116f0-2f92-4774-bda5-5471945803d0/24093000_EO_powerBI.png Project Blog - EO - Pt.1 - Make it stand out Example from a Power BI dashboard showing average land surface temperature by parcel type. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/9d0542b5-afed-4bda-ae24-2a0b88738c11/24093000_EO_3DepExamples.jpg Project Blog - EO - Pt.1 - Make it stand out Colorized USGS elevation data rendered with a DeckGL pointcloud layer in a React web application. https://digitalsloan.com/project-blog/urban-change-analysis-dynamic-world-v1 monthly 0.5 2024-09-08 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/d079ac3a-10be-493f-bb8c-e5f2139ec3a3/24090200_UCA_laredoTx_2018-2023.png Project Blog - Urban Change Analysis - Dynamic World V1 - Make it stand out Overall change between 2018 - 2023. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/3c38d8f4-c6df-40a9-a43f-53ae369d9131/24090800_UCA_2018-2023_zoom-01.jpg Project Blog - Urban Change Analysis - Dynamic World V1 - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1725822115646-010LLNF5PQBJWNHYHE89/24090200_UCA_laredoTx_2018-2019.png Project Blog - Urban Change Analysis - Dynamic World V1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1725822115676-G9XN45HEX41PM87BU341/24090200_UCA_laredoTx_2019-2020.png Project Blog - Urban Change Analysis - Dynamic World V1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1725822124084-3TSE8RJDITUDOYZT8UOF/24090200_UCA_laredoTx_2020-2021.png Project Blog - Urban Change Analysis - Dynamic World V1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1725822124590-WB0AT7P639IEA6IM9CR5/24090200_UCA_laredoTx_2021-2022.png Project Blog - Urban Change Analysis - Dynamic World V1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1725822129197-0R3C0L3ST791XPJBZ5UK/24090200_UCA_laredoTx_2022-2023.png Project Blog - Urban Change Analysis - Dynamic World V1 https://digitalsloan.com/project-blog/image-database-visualization monthly 0.5 2024-01-15 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/c37c734f-1778-4eee-9842-a61bf7421914/imageDBCoverImage.png Project Blog - Image Database Visualization https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/ab6bdba3-cd7e-4d5c-a138-b5ff60d07a4c/ImageDB_versions.jpg Project Blog - Image Database Visualization - Make it stand out V1: Version 1 was an early proof of concept/exploration into the idea of visualizing images as a 3D point cloud. In this version, images are represented by a single point in 3D space, using the color moments of the image to generate the X, Y, Z coordinates. Images were processed with Python and visualized with Grasshopper and Rhino as vector geometries in 3D Rhino space. V2: This version has some significant advancements over V1, most notably that images are visualized as bitmaps mapped to surfaces in 3D space, rather than as a single point. Grasshopper and Rhino were still used to visualize the images in 3D. V3: This version is a development of V2 and uses ThreeJS to visualize the images in a browser as well as provides interactive features including filtering images by labels and mouseover events to display the image hovered over. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/e5ac98a3-fa23-4efe-94ab-11ccd6626a32/greyScaleImageSlices.gif Project Blog - Image Database Visualization - Make it stand out The gif here shows a greyscale image molded in 3d where the greyscale value of the pixel is represented by the height of the extruded bar, being sliced into individual rows. Each row of pixel data is combined into a single long vector that will represent the image in the PCA calculations. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/7ec32489-1ec0-48ae-869c-24ad6404a81b/imageCovarianceMatrix.png Project Blog - Image Database Visualization - Make it stand out This covariance matrix depicts the covariance of variables for a subset of 100 images. The covariance between variables (in this case, features in the images) measures how much they vary together. A positive covariance indicates that when one variable is large, the other corresponding variable also tends to be large. The same is true with respect to negative values but in the other direction. https://digitalsloan.com/project-blog/mining-doe-contracts-pt1 monthly 0.5 2024-01-01 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/560f7171-ad9b-4354-8bcb-645a3746e36f/DOE_FY23_topRecipientsLogos_00.png Project Blog - Mining DOE Contracts - Pt.1 - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/e41c83e9-c996-47da-aade-bfb06ad18ee5/DOE_FY23_recipientNames.png Project Blog - Mining DOE Contracts - Pt.1 - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/2476e997-3afd-4c8d-b286-3ce4f351fc85/DOE_FY23_projectLocations.png Project Blog - Mining DOE Contracts - Pt.1 - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://digitalsloan.com/project-blog/tranportation-infrastructure monthly 0.5 2023-12-27 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/d611b9a4-9ae0-4d3f-8aec-0a4b05b59f9a/AV_seatCapacityAnalysis_allGroups.png Project Blog - Locating Infrastructure Projects with Data Mining - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/e84cb717-3a87-4931-8604-47118576f617/AV_seatCapacityAnalysis_northAmerica.png Project Blog - Locating Infrastructure Projects with Data Mining - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/0c693dba-d05c-4e86-92d3-da9c06315eac/SeatCapacity_filtering_00.png Project Blog - Locating Infrastructure Projects with Data Mining - Make it stand out The dashboard is set up so the user can filter the results of the seat capacity analysis by region and by aircraft seat group designation. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/121569a4-476f-4789-819e-075e0a18dfab/dataMining_dotContracts_00.png Project Blog - Locating Infrastructure Projects with Data Mining - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/f0f3cd23-aeaa-49e1-acf0-6c4a467654a1/DOT_awards_00.png Project Blog - Locating Infrastructure Projects with Data Mining The diagram here shows the top companies within the filtered NAICS categories by total award value, organized by sector. Here we can see the major sectors the DOT is contracting are IT services and consulting, engineering services, construction, business consulting and aviation/aerospace component manufacturing. https://digitalsloan.com/project-blog/landsat-8-pt2 monthly 0.5 2023-09-30 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/a30d96b3-0aa8-4cea-a47e-92dfacc53e80/23083000_landsat_process-01.jpg Project Blog - Landsat 8 - pt.2 - Make it stand out This diagram outlines a high level workflow, going from the Google Earth Engine to VSCode. Inside the Google Earth Engine, we use the JavaScript code editor to extract, filter and combine imagery and export it to Google Drive. We export the imagery as geoTiff (Geographic Tagged Image File Format) files. We also need to export the metadata of each geoTiff file which contain important information such as multipliers and constants used when calculating land surface temperature and other metrics. I exported this data as CSVs, but you can export them in whatever format best suits your workflow. Once the files are exported and saved to Google Drive, they can be downloaded and into your VSCode workspace for local processing. You can also process the files using cloud platforms like Google Colabs, but I prefer VSCode. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/75eb6128-ad78-40aa-bcd4-62ae4efef778/Screen+Shot+2023-09-30+at+5.39.16+PM.jpg Project Blog - Landsat 8 - pt.2 - Make it stand out My VSCode workspace is organized into four main parts as shown in the diagram. 00_resources This folder contains reference files/datasets such as geospatial reference data which are referenced by one or more scripts in the "src" folder. Reference files in this folder include the following: zipcidesToGet.txt This file is referenced if we want to break the main ROI extracted from the Google Earth Engine into smaller, sub ROIs. For example, if our main ROI encompasses a large city and we want to analyze data in specific neighborhoods, we can enter the zip codes of those neighborhood into this file. A python script references this file and searches for the zip codes in a geographic reference file from the Census Bureau (ZCTA Gazzetteer) and returns the interpolated centers of the zip codes. The interpolated centers are then used to construct the sub-roi bounding boxes which are written to the roi.json file. roi.json This file contains the bounding box coordinates of the main ROI and the sub-ROIs. This file is referenced by multiple Python scripts during the aggregation and analysis process. 01_data This folder is the main storage space for data we want to analyze and contains the GeoTiff files exported from the Google Earth Engine as well as any additional data such as GIS parcel data. For this exercise we will not be going into the parcel analysis, but that is something I'm working. 02_output: This folder will contain pre-processed and analyzed data. src The src folder contains the source code for any data pre-processing, analysis and visualization. As of the time of writing this post, scripts in this folder include the following: poolBands.py This script reads geoTiff files in the 01_data folder and runs a pooling window (of a size determined by the user) over the imager that pools data from select bands in the multispectral imagery and calculates the mean of the data within the window. This process is used to reduce the amount of data to process in the temporal analysis by reducing the number of data points in the imagery. It's important to note that this process with reduce the detail of the output but will improve processing speed. The processed data for each GeoTiff file is written to individual JSON files with the same name as the GeoTiff. normalizeLST.py This script reads the simplified imagery and normalizes land surface temperature data. This step is done so we can identify areas that have seen significant changes in thermal characteristics over time without being skewed by changes in weather from year to year. temporalAnalysis.py This script reads the pooled and normalized landsat data and calculates changes in LST and NDVI over the analysis period. The script also references the roi.json file and used the bounding box of the main roi to construct real-world coordinates to map the analyzed data to. The resulting output is written to the temporal_analysis.json file in the 02_output folder. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/b99e5ba8-1629-463d-8eef-98b461884608/Screen+Shot+2023-09-24+at+12.48.22+PM.png Project Blog - Landsat 8 - pt.2 - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/d032cc28-898c-4cc7-8ee5-395e836c9c4f/lawrenceKS_lstfDelta.png Project Blog - Landsat 8 - pt.2 - Make it stand out LST (Land Surface Temperature) https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/16a3b422-33af-467d-a7c6-e71ac9bc6f76/lawrenceKS_ndviDelta.png Project Blog - Landsat 8 - pt.2 - Make it stand out NDVI (Normalized Distribution Vegetation Index) https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1696116176641-WV3YTN2EX4359E1SQ04I/Screen+Shot+2023-09-23+at+6.35.59+PM.png Project Blog - Landsat 8 - pt.2 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1696116178678-6F9RR1CXHNFBUPDD5SIY/Screen+Shot+2023-09-24+at+11.47.27+AM.png Project Blog - Landsat 8 - pt.2 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1696116175628-J6V4W3B0MPXLNNATLD36/Screen+Shot+2023-09-23+at+6.32.33+PM.png Project Blog - Landsat 8 - pt.2 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1696116175448-N2374Z48GVMX9HCOZWMF/Screen+Shot+2023-09-23+at+5.56.24+PM.png Project Blog - Landsat 8 - pt.2 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1696116176910-LLL2CG1Q0ZGGIND2S1T0/Screen+Shot+2023-09-23+at+6.36.09+PM.png Project Blog - Landsat 8 - pt.2 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1696116178163-3BVZRHORCFKFY2WTUKZ3/Screen+Shot+2023-09-24+at+11.46.42+AM.png Project Blog - Landsat 8 - pt.2 https://digitalsloan.com/project-blog/cbri monthly 0.5 2023-09-18 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/c345433f-4e6d-4ec4-a938-0330d794f480/Screen+Shot+2023-09-17+at+6.14.32+PM.png Project Blog - Image Feature Extraction - Color Moment Indexing - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/9bb0cf31-5ebc-4d3f-885e-fcca4c72d28e/Screen+Shot+2023-09-10+at+1.23.32+PM.png Project Blog - Image Feature Extraction - Color Moment Indexing - Make it stand out The setup.py script, as the name suggests, is used to setup the workspace by creating any folders and subfolders needed. Subfolders: manifest The manifests folder will store files that contain information about the images processed. These manifests are used to quickly recall image meta data without needing to reprocess an image, as well as the results of any statistical analysis performed on an image which is used in the indexing, similarity measurements and clustering. images The images folder will contain all the reformatted images we want to process. As we will see later, images are copied from their original location, resized and saved to this folder. This is done for to avoid potentially corrupting/damaging the original image should something happen during processing and to resize the images to a smaller size to reduce the data processing load. images_grouped After images have been processed, clustered and labeled, images are shuttled to folders based on the label they've been assigned by the k-means clustering algorithm. paths.json The paths.json file contains the paths to each of the newly created folders and is used as a reference file when declaring paths in other scripts. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/2fd2ba48-8a17-4d14-8154-c757cf72abc7/Screen+Shot+2023-09-10+at+1.30.06+PM.png Project Blog - Image Feature Extraction - Color Moment Indexing - Make it stand out With the shuttleImages.py script, you can simply provide the directories of whatever images want to process by adding the directory paths to the "dirs_" list. The script will cycle through the list, navigate to the directory and will proceed to read over each file in the directory. The script will check if the file is an image. If the file is an image, the meta data such as the file's original name, size and date modified, will be written to the "imageStats_ref.json" file. The image will then be resized according to the maximum dimensions provided by the user and shuttled to the "images" folder. For the sake of consistency, the image is renamed so that all image names are of the same format and can easily be parsed later for other purposes. The "imageStats_ref.json" file, contains both the new and original name of the image, so we can use this file whenever we need to get information about the original image. Additionally, since this reference file will be used later when we recall the date/time the original image was taken. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/bdc7c129-fbba-4cfd-b409-6dddacb2da6e/Screen+Shot+2023-09-10+at+3.55.36+PM.png Project Blog - Image Feature Extraction - Color Moment Indexing - First-order - Variance The variance measures the spread of colors in the image and provides an idea of how much the distribution of colors deviate from the mean. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/cd5b5c4b-56df-4e56-888b-9def01fdf51a/Screen+Shot+2023-09-10+at+3.55.52+PM.png Project Blog - Image Feature Extraction - Color Moment Indexing - Second-order - Skewness Skewness measures the asymmetry of the distribution of values in a given color channel. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/ddb28e51-3f5c-43a7-9f9a-4e067c968bb5/Screen+Shot+2023-09-10+at+4.15.14+PM.png Project Blog - Image Feature Extraction - Color Moment Indexing - Third-order - Kurtosis: Kurtosis measures length of the "tail" of the distribution. Kurtosis provides a more detailed understanding about the shape of the distribution such as whether the distribution is more peaked or more flat. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/4a17d4ab-9732-4076-b928-79523aa04a9a/Screen+Shot+2023-09-10+at+4.25.34+PM.png Project Blog - Image Feature Extraction - Color Moment Indexing - Make it stand out First we have the calc_color_moments function, which, as mentioned before is responsible for calculating color moments of an input image. Next we have the calc_midpoint function, which is used when it comes time to generate a single point that will represent the image in 3D space. Since we are calculating color moments for each of the three color channels (red, green and blue), we will have three sets of data, each with three color moments. The color moment values are treated as X,Y,Z coordinates and used to generate a point in 3D space to represent the color moments of their respective channel. To generate a single point that represents all the color channels, we will calculate the midpoint between the three points. This function takes the coordinates of the three points as its input and returns a new point as X,Y,Z coordinates as a tuple. Finally, the normalize_val function is used to normalize an input value between zero and one. Since color moments values can have significantly different ranges, we will want to normalize the data before treating the color moments as coordinates, that way we can plot them all in the same 3D space. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/9c6511c6-67e3-4379-ac44-75ae5d9e0f98/Screen+Shot+2023-09-10+at+4.40.37+PM.png Project Blog - Image Feature Extraction - Color Moment Indexing - Make it stand out In this block of code, the script will read each image in the given image directory. Since the images in the given directory have been resized and saved as a new image, the date created and modified data is different from the original image. Since I want to keep the dates of the original image associated with the new, reduced size copy of the image, we can reference the "imageStats_ref.json" file to grab the dates of the original image. Next, the image is passed to the calc_color_moments function and the results are parsed. A new item with a unique image id is added to the imageStats_ dictionary, where we will add the color moment calculations to their respective channels. Notice that there are additional color moment values in the newly made dictionary item with a the "_norm" designation. These values are set to "None" by default, but will be replaced with the normalized value for each color moment later. In order to normalize the data, we need to get the data ranges so we append the repetitive lists in the "poolData_" dictionary with the color moments. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/fb4c12e2-0bf6-4376-abe7-1da7475b07aa/Screen+Shot+2023-09-10+at+4.52.49+PM.png Project Blog - Image Feature Extraction - Color Moment Indexing - Make it stand out As mentioned previously, we will need to normalize the color moment data prior to using the data as coordinates for plotting points in 3D space. In order to normalize the data between zero and one, we will need to get the data ranges for each color moment by calculating the minimum and maximum values seen across all the images processed. To do this, we can simply use the built-in min and max methos on the lists contained within the poolData_ dictionary. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/d7a55639-179f-4849-be09-a9825e8f372b/Screen+Shot+2023-09-10+at+4.57.33+PM.png Project Blog - Image Feature Extraction - Color Moment Indexing - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/cbd4c67f-8fcb-4045-8adb-e0eb5b193da9/Screen+Shot+2023-09-10+at+5.03.59+PM.png Project Blog - Image Feature Extraction - Color Moment Indexing - Make it stand out Finally, we can write the results to a json. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/45f9d62e-cfb8-498c-8846-b9191f9f0b4a/Screen+Shot+2023-09-17+at+6.26.11+PM.png Project Blog - Image Feature Extraction - Color Moment Indexing - Make it stand out This is the Grasshopper definition used to generate the visualization. The definition is split into four parts. 01: Read the image stats JSON with the Grasshopper Python node and parse the results into the outputs (a,b,c,d,e,f,g) 02: Get the unique id of each image and visualize the image id at the centroid of where the image will be visualized in 3d space. 03: Construct 3d points calculated from the color moments of the image (see the imageStats.py script). At each point, generate a plane which will be used to map the image to. The plane should be oriented vertically along the X/Z axis. 04: Render images in 3d by mapping the image to the planes generated in part 03. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/3c510aa3-ac5e-4ec4-8617-a925fb0c7b6a/Screen+Shot+2023-09-17+at+6.47.41+PM.png Project Blog - Image Feature Extraction - Color Moment Indexing - Make it stand out Here is what the images look like once rendered in 3D space. It appears model is working, as we can see that images of similar color distributions are plotted in similar places in the cloud of images. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/d850c953-a225-485e-9dcd-a5d6c2d01909/Screen+Shot+2023-09-17+at+7.18.55+PM.png Project Blog - Image Feature Extraction - Color Moment Indexing - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://digitalsloan.com/project-blog/landsat-8-pt1 monthly 0.5 2023-09-09 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1693871082468-1OUU7OKKFF3Q7UM5KBIM/23090400_landsat_composite_dfw_2015-01.jpg Project Blog - Landsat 8 - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1693871082504-NW04D2GXRRW0RS6OBC4W/23090400_landsat_composite_dfw_2016-01.jpg Project Blog - Landsat 8 - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1693871085030-I2FYIKRMDSVF6Z81SH5G/23090400_landsat_composite_dfw_2017-01.jpg Project Blog - Landsat 8 - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1693871085122-25E6GOV97J697IE1PCNS/23090400_landsat_composite_dfw_2018-01.jpg Project Blog - Landsat 8 - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1693871087307-JD5X03V6BS3I6P7IKOZR/23090400_landsat_composite_dfw_2019-01.jpg Project Blog - Landsat 8 - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1693871087366-R1HXZ644QC5XA8VDDWS0/23090400_landsat_composite_dfw_2020-01.jpg Project Blog - Landsat 8 - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1693871089846-QQ6V40U0R9Z449S9RKQ2/23090400_landsat_composite_dfw_2021-01.jpg Project Blog - Landsat 8 - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1693871089861-56Y3I0XWYSL7IZL2XDYR/23090400_landsat_composite_dfw_2022-01.jpg Project Blog - Landsat 8 - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1693871091504-BZE85A1IXGLJ1ZTM0QKA/23090400_landsat_composite_dfw_2023-01.jpg Project Blog - Landsat 8 - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/f43f8cf1-325f-414c-a448-4e408f474ca0/Screen+Shot+2023-09-09+at+3.59.59+PM.png Project Blog - Landsat 8 - pt.1 - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/faa57544-6d75-42c3-a23d-8d16b14631a8/Screen+Shot+2023-09-09+at+4.28.58+PM.png Project Blog - Landsat 8 - pt.1 NDVI is calculated by simply taking the difference of the near-infrared (NIR) band and the red band, divided by the sum of near-infrared and the red band. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1694295606652-8UTV0TJMRXM5592UEHK4/Dallas-01.jpg Project Blog - Landsat 8 - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1694295606618-L72B1R6ZSZMSBYKY9A59/El+Paso-01.jpg Project Blog - Landsat 8 - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1694295609000-2ZWOLRTB9DE1X1V1VKWD/Mexico+City-01.jpg Project Blog - Landsat 8 - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1694295610473-3PSPCB3WM8XVP60Q1FOX/Miami-01.jpg Project Blog - Landsat 8 - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1694295610819-6D08EUWU4ZJOTYMMPY2D/San+Francisco-01.jpg Project Blog - Landsat 8 - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1694295613136-Q5FCWYB7IEWQ1HD4DI46/Sa%CC%83o+Paulo-01.jpg Project Blog - Landsat 8 - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1694295613095-9MV5HFDHGMA3L36YS2YK/Singapore-01.jpg Project Blog - Landsat 8 - pt.1 https://digitalsloan.com/project-blog/kycdlpjynudih4i39pzi9np6bwv8fz monthly 0.5 2023-07-23 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1a5c0088-7498-4aea-ac52-7922ebe768f3/Screen+Shot+2023-07-16+at+6.31.20+PM.png Project Blog - Analyzing Federal Spending - pt.1 - USASpending.GOV - Award Data Archive Screenshot from USASpending.Gov https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/86babdd8-dbfc-45e4-bade-5dd2ce16458b/pyFunc_formatZipcode.png Project Blog - Analyzing Federal Spending - pt.1 - The zipcode formatting python function https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/60678bf2-1ddf-4bff-8177-f51896584a32/GeoAggregation_zipcodes.png Project Blog - Analyzing Federal Spending - pt.1 - Zipcodes https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/a854044a-d46a-483e-a904-8f11280403e3/GeoAggregation_zipcodesAndCBSAs.png Project Blog - Analyzing Federal Spending - pt.1 - Zipcodes (cyan) and CBSAs (magenta) https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/5ecef96e-660c-47e7-bcc9-acbd2ef1a8cc/GeoAggregation_closestCBSA.png Project Blog - Analyzing Federal Spending - pt.1 - KNN - Nearest CBSA To reduce the number of geographic to compare in the final output, contract data is aggregated with the nearest CBSA. This is simply calculated using a nearest neighbor algorithm and the results are written to geojson. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1690147895189-OT9VIRSEJ06DD19Y6S46/GeoAggregation_closestCBSA_florida.png Project Blog - Analyzing Federal Spending - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1690147896882-0HRO9DJ1MGAY01RYX961/GeoAggregation_closestCBSA_midwest.png Project Blog - Analyzing Federal Spending - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/9b8ad9fe-0e50-4b93-b3b8-4ae348c12b32/json_naicsCodes.png Project Blog - Analyzing Federal Spending - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/4a89f401-db7e-4479-af27-5ddfff3b561c/outputCSV.png Project Blog - Analyzing Federal Spending - pt.1 - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1690070730546-HJ1C487LGD0NNJTB1VBJ/KeplerGL_US.png Project Blog - Analyzing Federal Spending - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1690070729227-64MKAEH6VQLDIDO023FN/KeplerGL_Chicago.png Project Blog - Analyzing Federal Spending - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1690070730172-AEIMW8S732C5P15G697W/KeplerGL_Denver.png Project Blog - Analyzing Federal Spending - pt.1 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1690070729022-7XCWUBV8K3TMIQBWDYEX/KeplerGL_BayArea.png Project Blog - Analyzing Federal Spending - pt.1 https://digitalsloan.com/project-blog/yolov8-test monthly 0.5 2023-06-11 https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/556b0749-f940-42fc-8da9-05bb264e2d4c/detection_beach-01.png Project Blog - Image Characteristics with YOLOV8 - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/08bb047b-a7ca-4299-825e-462ac59d1989/Screen+Shot+2023-06-11+at+5.40.11+PM.png Project Blog - Image Characteristics with YOLOV8 - Imports and directory paths Begin by importing the required libraries. Set the “extractMetaData” variable to True if you wish to extract meta data from the images or to False if you wish to skip that step. Set the directory paths for additional resource, input data and data output locations. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/385b9c6c-ca81-4bf8-9af0-9fd741277080/Screen+Shot+2023-06-11+at+5.09.37+PM.png Project Blog - Image Characteristics with YOLOV8 - Set the pre-trained model and data input path Select the appropriate pre-trained YOLO model. For this project I’m using the “yoloV8m.pt but I may switch to the large version for more accuracy. Get the directory path of the images to process from the workspace path dictionary and store the path as in a variable since that path is called multiple times. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/c3ef67b1-8f71-480d-a86c-8ec0390ba382/Screen+Shot+2023-06-11+at+5.15.05+PM.png Project Blog - Image Characteristics with YOLOV8 - Define functions This function will be used during the meta data extraction step. The default format for image geolocation data is in (degrees, minutes, seconds). The function take the geolocation data and converts it to latitude and longitude decimal coordinates. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/4778d3e5-35fb-481c-a819-e05008672c88/Screen+Shot+2023-06-11+at+5.21.35+PM.png Project Blog - Image Characteristics with YOLOV8 - Create the manifest and begin cycling over input images The manifest dictionary will store any meta data extracted from an image and detections. With a for loop, cycle over each file in the given directory. If the file is an acceptable format, continue processing. Create a new dictionary object for each image. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/8421999e-ae9e-4933-80dc-ae36149a0c58/Screen+Shot+2023-06-11+at+5.26.27+PM.png Project Blog - Image Characteristics with YOLOV8 - Extract meta data Using PIL, check if the image has accessible meta data. If the image has accessible meta data, cycle through the meta data tags and check if the tag matches data categories we want to extract. If the tag indicates geolocation, pass the geolocation metadata to the decimal_coords function and write the returned coordinates to the image manifest. If the tag indicates datetime, parse the datetime data into individual components and write them to the image manifest. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/1cdc9470-1edf-4036-987d-683ff3f37bbc/Screen+Shot+2023-06-11+at+5.29.44+PM.png Project Blog - Image Characteristics with YOLOV8 - Resize the image and use YOLO to detect objects The YOLO model selected is designed for images with a maximum dimension of 640 pixels. Use PIL to resize the image so the maximum dimension is equal to a given pixel dimension that is less than or equal to 640 pixels. The resized image is saved to the given directory. Pass the resized image to the YOLO model and parse the results. For each detection in the list of results, get the detection class, coordinates of the bounding box and confidence metric and write them to the image manifest. Finally, once all images in the given directory have been processed, write the image manifest as a json file. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/135154b2-0a78-424e-8222-2d5823e0f612/Screen+Shot+2023-06-11+at+5.45.26+PM.png Project Blog - Image Characteristics with YOLOV8 - Manifest example This snippet is from the image manifest and shoes the meta data and detections from one of the processed images https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/313c036d-3960-4796-bbaa-a19058010b98/detection_ship-01.png Project Blog - Image Characteristics with YOLOV8 - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e3276971e893f0fcb52a490/a3b2d8a2-c9ee-42b0-8b48-2ac760ea144d/detection_crow-01.png Project Blog - Image Characteristics with YOLOV8 - Make it stand out Whatever it is, the way you tell your story online can make all the difference. 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