# US 11,308,603 B2 · Detection and monitoring of waste disposal in an environmental location using artificial intelligence > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,308,603 B2 - Document ID: US11308603B2 - Status: Granted - Kind: B2 - Application number: 16/991,022 - Filed: 2020-08-12 - Published: 2022-02-17 - Granted: 2022-04-19 - Earliest claimed: 2020-08-12 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Na Liu; Mei Rui Su; Pei Jian Liu; Bing Hua Zhao; Yan Liu; Zhong Fang Yuan; Wen Wang - Web page: https://patentgrep.com/patent/US11308603B2 ## CPC - G06T 7/0004: {Industrial image inspection} - G01N 33/18: Water - G06T 7/246: using feature-based methods, e.g. the tracking of corners or segments - G06V 20/13: Satellite images - G06T 2207/10032: Satellite or aerial image; Remote sensing - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Monitoring of an environmental location to detect waste disposal includes receiving, by a computer, a collection of images from aerial data acquisition sources, the collection of images corresponding to the environmental location and the waste disposed in the environmental location. The collection of images are processed by the computer to extract properties of the waste and first properties of the environmental location. Subsequently, the properties of the waste disposed in the environmental location are classified according to a class. Additional information from external data sources including second properties of the environmental location is received by the computer. The computer determines a behavior of waste disposal in the environmental location over a period of time based on the classified properties of the disposed waste and the first and second properties of the environmental location, and generated a remediation plan based on the determined behavior. ## Claims ### Claim 1 A computer-implemented method for monitoring and detecting waste disposal in an environmental location, comprising: receiving, by a computer, a collection of images from aerial data acquisition sources, the collection of images corresponding to the environmental location and a waste disposed in the environmental location, wherein the environmental location includes a water body; processing, by the computer, the collection of images to extract data comprising properties of the waste and first properties of the environmental location, the first properties of the environmental location including geographic coordinates and a temporal information associated with the environmental location; classifying, by the computer, the properties of the waste disposed in the environmental location according to a class; receiving, by the computer, additional information from external data sources, the additional information comprising second properties of the environmental location, the second properties of the environmental location including a water flow direction, a velocity, and a depth associated with the environmental location; determining, by the computer, a behavior of waste disposal in the environmental location over a period of time based on the classified properties of the waste disposed in the environmental location and the first and second properties of the environmental location; and generating, by the computer, a remediation plan according to the determined behavior of waste disposal in the environmental location. ### Claim 2 (depends on 1) The method of claim 1, further comprising: predicting, by the computer, a waste accumulation frequency in the environmental location over the period of time. ### Claim 3 (depends on 1) The method of claim 1, wherein the aerial data acquisition sources comprise unmanned aerial vehicles and satellite-based systems. ### Claim 4 (depends on 1) The method of claim 1, wherein the properties of the waste comprise geographic coordinates, a type and a size of the waste disposed in the environmental location. ### Claim 5 (depends on 1) The method of claim 1, wherein classifying the properties of the waste disposed in the environmental location is performed using a Convolutional Neural Network model. ### Claim 6 (depends on 1) The method of claim 1, wherein determining the behavior of waste disposal in the environmental location over the period of time is performed using a Recurrent Neural Network model. ### Claim 7 A computer system for monitoring and detecting waste disposal in an environmental location, comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising: receiving, by a computer, a collection of images from aerial data acquisition sources, the collection of images corresponding to the environmental location and a waste disposed in the environmental location, wherein the environmental location includes a water body; processing, by the computer, the collection of images to extract data comprising properties of the waste and first properties of the environmental location, the first properties of the environmental location including geographic coordinates and a temporal information associated with the environmental location; classifying, by the computer, the properties of the waste disposed in the environmental location according to a class; receiving, by the computer, additional information from external data sources, the additional information comprising second properties of the environmental location, the second properties of the environmental location including a water flow direction, a velocity, and a depth associated with the environmental location; determining, by the computer, a behavior of waste disposal in the environmental location over a period of time based on the classified properties of the waste disposed in the environmental location and the first and second properties of the environmental location; and generating, by the computer, a remediation plan according to the determined behavior of waste disposal in the environmental location. ### Claim 8 (depends on 7) The computer system of claim 7, further comprising: predicting, by the computer, a waste accumulation frequency in the environmental location over the period of time. ### Claim 9 (depends on 7) The computer system of claim 7, wherein the aerial data acquisition sources comprise unmanned aerial vehicles and satellite-based systems. ### Claim 10 (depends on 7) The computer system of claim 7, wherein the properties of the waste comprise geographic coordinates, a type and a size of the waste. ### Claim 11 (depends on 7) The computer system of claim 7, wherein classifying the properties of the waste disposed in the environmental location is performed using a Convolutional Neural Network model. ### Claim 12 (depends on 7) The computer system of claim 7, wherein determining the behavior of waste disposal in the environmental location over the period of time is performed using a Recurrent Neural Network model. ### Claim 13 A computer program product for monitoring and detecting waste disposal in an environmental location, comprising: one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising: program instructions to receive, by a computer, a collection of images from aerial data acquisition sources, the collection of images corresponding to the environmental location and a waste disposed in the environmental location, wherein the environmental location includes a water body; program instructions to process, by the computer, the collection of images to extract data comprising properties of the waste and first properties of the environmental location, the first properties of the environmental location including geographic coordinates and a temporal information associated with the environmental location; program instructions to classify, by the computer, the properties of the waste disposed in the environmental location according to a class; program instructions to receive, by the computer, additional information from external data sources, the additional information comprising second properties of the environmental location, the second properties of the environmental location including a water flow direction, a velocity, and a depth associated with the environmental location; program instructions to determine, by the computer, a behavior of waste disposal in the environmental location over a period of time based on the classified properties of the waste disposed in the environmental location and the first and second properties of the environmental location; and program instructions to generate, by the computer, a remediation plan according to the determined behavior of waste disposal in the environmental location. ### Claim 14 (depends on 13) The computer program product of claim 13, further comprising: program instructions to predict, by the computer, a waste accumulation frequency in the environmental location over the period of time. ### Claim 15 (depends on 13) The computer program product of claim 13, wherein the aerial data acquisition sources comprise unmanned aerial vehicles and satellite-based systems. ### Claim 16 (depends on 13) The computer program product of claim 13, wherein the properties of the waste comprise geographic coordinates, a type and a size of the waste. ### Claim 17 (depends on 13) The computer program product of claim 13, wherein the program instructions to classify the properties of the waste disposed in the environmental location comprise program instructions to use a Convolutional Neural Network model, and wherein the program instructions to determine the behavior of waste disposal in the environmental location over the period of time comprise program instructions to use a Recurrent Neural Network model.