According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising an image data source storing image data from a plurality of images; a height map source storing height maps for an area of interest (AOI); a building footprint module; a memory; and a building footprint processor, operative to execute the program instructions to: receive image data for an AOI; receive a height map for the AOI; execute a building segmentation module to generate a building mask that indicates a presence of one or more buildings in the AOI; apply at least one clean mask process to the generated building mask to generate a clean mask; receive the clean mask at an instance building segmentation module; and execute the instance building segmentation module to generate at least one building footprint based on the clean mask and the received image data. Numerous other aspects are provided.

A self-calibrating spectrometer that captures a sample spectrum image of a sample via a light dispersion device and a calibration spectrum image of a calibration light source having a known spectrum (e.g., in the same image frame using a bifurcated fiber optic cable). Spectral data is extracted from the sample spectrum image and wavelength calibrated by matching calibration spectral data extracted from the calibration spectrum image to the known spectrum of the calibration light source, mapping each pixel position of the calibration spectrum image to a wavelength of the known spectrum of the calibration light source, and mapping each pixel position of the sample spectral data to a wavelength based on the pixel position-to-wavelength mapping. In some embodiments, extracted features from the wavelength calibrated spectral data are used by classification module, trained on a dataset of features extracted from spectral data of known samples, to classify the sample.

Techniques are described for automated operations to determine tree inventory information for an area of land using visual data of overhead image(s), such as by using a trained prediction model specific to an ecological region to which the land area belongs as part of probabilistically determining multiple types of information about trees in that land area, and for subsequently using the determined tree inventory information in one or more manners (e.g., to improve management of trees in that land area). The images may, for example, include spectral satellite images that include at least visible light data for an area of land, and the determined tree inventory information may include information about the trees present on the land area, such as, for example, predictions of particular tree species, quantities of each of the tree species, sizes of the trees, etc.

This invention shows a system and method for phenotype characterization of agricultural crops, comprising at least one support device which can be reconfigured in an autonomous or controlled remote manner. It includes a central embedded microcontroller connected to atmospheric sensors located in the upper body, a microcontroller connected to a multi-spectral camera located at the distal end of the arm. The central microcontroller is connected to a base microcontroller that receives signals from soil sensors. A solar panel provides the energy source to a regulating unit that powers the microcontrollers. The system contains a communication unit that includes a router wirelessly connected to Internet.

A system for inspecting an aircraft includes a drone, a base station, and a controller. The drone includes one or more cameras. The base station has a storage compartment configured to store the autonomous drone therein. The controller has a processor and a memory. The memory has instructions stored thereon, which when executed by the processor, cause the base station to drive to a first predetermined location relative to the aircraft, and cause the drone to fly from the storage compartment of the base station to a first predetermined position relative to the aircraft so that the drone can record image data of at least portions of the aircraft with the one or more cameras.

A system and method for assessing categorized pixels of satellite images associated with agriculture land parcel using an artificial intelligence (AI) model are provided. The method includes, (i) obtaining satellite images associated with agriculture land parcel; (ii) pre-processing the satellite images to generate pre-processed satellite images, (iii) training the AI model by categorizing historical plurality of pixels from historical plurality of satellite images based on historical satellite data and correlating historical scores to historical categorized pixels to obtain trained AI model, (iv) classifying pixels of pre-processed satellite images into crop area-pixels and non-crop area pixels by determining a profile of time series data that corresponds to at least one of normalized difference vegetation index, normalized difference water index, land surface temperature, modified normalized difference water index, or land surface water index, (v) determining, using trained AI model, categorized pixels based on classification, (vi) assessing categorized pixels with a score.

A system for detecting clouds and cloud shadows is described. In one approach, clouds and cloud shadows within a remote sensing image are detected through a three step process. In the first stage a high-precision low-recall classifier is used to identify cloud seed pixels within the image. In the second stage, a low-precision high-recall classifier is used to identify potential cloud pixels within the image. Additionally, in the second stage, the cloud seed pixels are grown into the potential cloud pixels to identify clusters of pixels which have a high likelihood of representing clouds. In the third stage, a geometric technique is used to determine pixels which likely represent shadows cast by the clouds identified in the second stage. The clouds identified in the second stage and the shadows identified in the third stage are then exported as a cloud mask and shadow mask of the remote sensing image.

A platform for facilitating development of intelligence in an Industrial Internet of Things (IIoT) system generally includes a plurality of distinct data-handling layers comprising an industrial monitoring systems layer that collects data from or about a plurality of industrial entities in the IIoT system; an industrial entity-oriented data storage systems layer that stores the data collected by the industrial monitoring systems layer; an adaptive intelligent systems layer that provisions available computing resources within the platform; and an industrial management application platform layer that manages the platform in a common application environment.

In an embodiment, a computer-implemented method for predicting subfield soil properties for an agricultural field comprises: receiving satellite remote sensing data that includes a plurality of images capturing imagery of an agricultural field in a plurality of optical domains; receiving a plurality of environmental characteristics for the agricultural field; generating a plurality of preprocessed images based on the plurality of satellite remote sensing data and the plurality of environmental characteristics; identifying, based on the plurality preprocessed images, a plurality of features of the agricultural field; generating a subfield soil property prediction for the agricultural field by executing one or more machine learning models on the plurality of features; transmitting the subfield soil property prediction to an agricultural computer system.

Methods, systems, and apparatus, including computer programs encoded on a storage device, for filling gaps in electric grid models are enclosed. A method includes obtaining vector data representing first portions of paths of electric grid wires over a geographic region; converting the vector data to first raster image data that depicts an overhead view of the electric grid wires including a first set of line segments representing the first portions of the paths; processing the first raster image data using a gap filling model; obtaining, as output from the gap filling model, second raster image data including a second set of line segments corresponding to gaps included in the input raster image data and representing second portions of paths of the electric grid wires; and converting the second raster image data to vector data representing the first portions and the second portions of paths of the electric grid wires.