US 12,437,196 B2
Method and system for estimating urban metabolism
Shailesh Shankar Deshpande, Pune (IN); Chaman Banolia, New Delhi (IN); and Balamuralidhar Purushothaman, Bangalore (IN)
Assigned to TATA CONSULTANCY SERVICES LIMITED, Mumbai (IN)
Filed by Tata Consultancy Services Limited, Mumbai (IN)
Filed on Oct. 21, 2022, as Appl. No. 17/970,975.
Claims priority of application No. 202121059475 (IN), filed on Dec. 20, 2021.
Prior Publication US 2023/0196099 A1, Jun. 22, 2023
Int. Cl. G06K 9/62 (2022.01); G06N 3/08 (2023.01); G06T 5/80 (2024.01); G06T 7/00 (2017.01)
CPC G06N 3/08 (2013.01) [G06T 5/80 (2024.01); G06T 7/0002 (2013.01); G06T 2207/10032 (2013.01); G06T 2207/20081 (2013.01)] 7 Claims
OG exemplary drawing
 
6. A system comprising:
an input/output interface to receive remotely sensed data and a predefined ecological dataset of a predefined urban area, wherein the remotely sensed data includes multispectral data, hyperspectral data, radar data, one or more Landsat-8 images and shapefiles of the predefined urban area:
one or more hardware processors;
a memory in communication with the one or more hardware processors, wherein the one or more hardware processors are configured to execute programmed instructions stored in the memory, to:
filter the received predefined ecological dataset based on null values and an emission availability to structure the ecological dataset into a predefined format of a mapping table;
process the structured ecological dataset based on one or more predefined automatic scripts;
analyze the received remotely sensed data according to the shapefiles of the predefined urban area using a machine learning technique and one or more fractions of classes in each pixel of the remotely sensed data;
classify the one or more Landsat-8 images based on a support vector machine to obtain a dataset of vegetation, impervious surfaces, and soil;
train a regression model using the pivoted dataset and a land cover, and
determine one or more urban metabolic parameters using the trained regression model, wherein the one or more urban metabolic parameters of present and future are as per a simple temporal or spatial scenario including carbon emission of the predefined region.