Abstract: In variants, a method for property group analysis can include: determining a property, determining whether the property is part of a group, identifying other properties within the group, optionally determining whether to merge groups, and optionally providing a final group. However, the method can additionally and/or alternatively include any other suitable elements.

Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.

Abstract: In variants, the method for automatic debris detection includes: determining a region image; optionally determining a parcel representation for the region image; generating a debris representation using the region image; generating a debris score based on the debris representation; and optionally monitoring the debris score over time.

Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.

Abstract: In variants, a method for property group analysis can include: determining a property, determining whether the property is part of a group, identifying other properties within the group, optionally determining whether to merge groups, and optionally providing a final group. However, the method can additionally and/or alternatively include any other suitable elements.

Abstract: In variants, the method for property condition analysis can include: determining a measurement, optionally determining a set of property attributes, determining a condition score, optionally providing the condition score, and optionally training a condition scoring model.

Abstract: In variants, the method for change analysis can include detecting a rare change in a geographic region by comparing a first representation and a second representation, extracted from a first geographic region measurement and a second geographic region measurement sampled at a first time and a second time, respectively, using a common-change-agnostic model.

Abstract: Systems and methods are provided for automatically detecting a change in a feature. For example, a system includes a memory and a processor configured to analyze a change associated with a feature over a period of time using a plurality of remotely sensed time series images. Upon execution, the system would receive a plurality of remotely sensed time series images, extract a feature from the plurality of remotely sensed time series images, generate at least two time series feature vectors based on the feature, where the at least two time series feature vectors correspond to the feature at two different times, create a neural network model configured to predict a change in the feature at a specified time, and determine, using the neural network model, the change in the feature at a specified time based on a change between the at least two time series feature vectors.

Abstract: In variants, the method can include: determining a timeseries of measurements of a geographic region; determining a set of object representations from the timeseries of measurements; and determining a timeseries of object versions based on relationships between the object representations.

Abstract: In variants, the method for change analysis can include: training a representation model and evaluating a geographic region. In an example, the method for change analysis can include detecting a rare change in a geographic region by comparing a first and second representation, extracted from a first and second geographic region measurement sampled at a first and second time, respectively, using a common-change-agnostic model.

Abstract: Systems and methods are provided for automatically detecting a change in a feature. For example, a system includes a memory and a processor configured to analyze a change associated with a feature over a period of time using a plurality of remotely sensed time series images. Upon execution, the system would receive a plurality of remotely sensed time series images, extract a feature from the plurality of remotely sensed time series images, generate at least two time series feature vectors based on the feature, where the at least two time series feature vectors correspond to the feature at two different times, create a neural network model configured to predict a change in the feature at a specified time, and determine, using the neural network model, the change in the feature at a specified time based on a change between the at least two time series feature vectors.

Abstract: In variants, the method for 3D modeling can include: determining a property of interest, determining property information for the property of interest, determining property component parameter values based on property information, determining a 3D model based on property component parameter values, and optionally determining a set of property attributes. However, the method can additionally and/or alternatively include any other suitable elements.

Abstract: Systems and methods are provided for automatically detecting a change in a feature. For example, a system includes a memory and a processor configured to analyze a change associated with a feature over a period of time using a plurality of remotely sensed time series images. Upon execution, the system would receive a plurality of remotely sensed time series images, extract a feature from the plurality of remotely sensed time series images, generate at least two time series feature vectors based on the feature, where the at least two time series feature vectors correspond to the feature at two different times, create a neural network model configured to predict a change in the feature at a specified time, and determine, using the neural network model, the change in the feature at a specified time based on a change between the at least two time series feature vectors.

Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.

Abstract: In variants, the method for subjective property scoring can include determining an objective score for a subjective characteristic of a property using a model trained using subjective labels for a set of training properties. In examples, the model can be trained on subjective property rankings, determined using the subjective labels, for the set of training properties.

Abstract: In variants, the method for property condition analysis can include: determining a measurement, optionally determining a set of property attributes, determining a condition score, optionally providing the condition score, and optionally training a condition scoring model.

Abstract: In variants, the method for change analysis can include detecting a rare change in a geographic region by comparing a first and second representation, extracted from a first and second geographic region measurement sampled at a first and second time, respectively, using a common-change-agnostic model.

Abstract: In variants, the method for property condition analysis can include: determining a measurement, optionally determining a set of property attributes, determining a condition score, optionally providing the condition score, and optionally training a condition scoring model.

Abstract: In variants, the method for subjective property scoring can include determining an objective score for a subjective characteristic of a property using a model trained using subjective property rankings.

Abstract: In variants, the method for property condition analysis can include: determining a measurement, optionally determining a set of property attributes, determining a condition score, optionally providing the condition score, and optionally training a condition scoring model.