DIVERSITY QUANTIFICATION
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for Measuring and Quantifying Biodiversity in an Environment. One of the methods includes receiving a set of images representing the marine environment; identifying, by one or more processing devices within the set of images, objects representing marine life in the marine environment; classifying, by the one or more processing devices, the objects into multiple clusters based on feature vectors identified for each of the objects; and computing, by the one or more processing devices based on attributes associated with the multiple clusters, a metric indicative of the biodiversity in the marine environment.
1 . A method of estimating biodiversity in a marine environment, the method comprising:
receiving a set of images representing the marine environment;
identifying, by one or more processing devices within the set of images, objects representing marine life in the marine environment;
classifying, by the one or more processing devices, the objects into multiple clusters based on feature vectors identified for each of the objects; and
computing, by the one or more processing devices based on attributes associated with the multiple clusters, a metric indicative of the biodiversity in the marine environment.
2 . The method of claim 1 , wherein the set of images include images captured by a remotely operated vehicle.
3 . The method of claim 1 , wherein the set of images include hyperspectral images.
4 . The method of claim 1 , wherein the feature vectors include vectors of locally aggregated descriptors (VLADs) generated by processing representation of the objects by a convolutional neural network.
5 . The method of claim 1 , wherein the attributes associated with the multiple clusters include one or more of: a number of clusters, a distribution of the multiple clusters, a spread among the multiple clusters, and a spread within a particular cluster of the multiple clusters.
6 . The method of claim 1 , wherein the objects are classified into multiple clusters using a supervised machine learning model trained on a corpus of labeled data set identifying various forms of marine life.
7 . The method of claim 1 , wherein the metric represents differences among the multiple clusters as computed based on multiple dimensions within the feature vectors.
8 . A system comprising one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations for estimating biodiversity in a marine environment comprising:
receiving a set of images representing the marine environment;
identifying, by one or more processing devices within the set of images, objects representing marine life in the marine environment;
classifying, by the one or more processing devices, the objects into multiple clusters based on feature vectors identified for each of the objects; and
computing, by the one or more processing devices based on attributes associated with the multiple clusters, a metric indicative of the biodiversity in the marine environment.
9 . The system of claim 8 , wherein the set of images include images captured by a remotely operated vehicle.
10 . The system of claim 8 , wherein the set of images include hyperspectral images.
11 . The system of claim 8 , wherein the feature vectors include vectors of locally aggregated descriptors (VLADs) generated by processing representation of the objects by a convolutional neural network.
12 . The system of claim 8 , wherein the attributes associated with the multiple clusters include one or more of: a number of clusters, a distribution of the multiple clusters, a spread among the multiple clusters, and a spread within a particular cluster of the multiple clusters.
13 . The system of claim 8 , wherein the objects are classified into multiple clusters using a supervised machine learning model trained on a corpus of labeled data set identifying various forms of marine life.
14 . The system of claim 8 , wherein the metric represents differences among the multiple clusters as computed based on multiple dimensions within the feature vectors.
15 . One or more non-transitory computer storage media encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations for estimating biodiversity in a marine environment comprising:
receiving a set of images representing the marine environment;
identifying, by one or more processing devices within the set of images, objects representing marine life in the marine environment;
classifying, by the one or more processing devices, the objects into multiple clusters based on feature vectors identified for each of the objects; and
computing, by the one or more processing devices based on attributes associated with the multiple clusters, a metric indicative of the biodiversity in the marine environment.
16 . The computer storage media of claim 15 , wherein the set of images include images captured by a remotely operated vehicle.
17 . The computer storage media of claim 15 , wherein the set of images include hyperspectral images.
18 . The computer storage media of claim 15 , wherein the feature vectors include vectors of locally aggregated descriptors (VLADs) generated by processing representation of the objects by a convolutional neural network.
19 . The computer storage media of claim 15 , wherein the attributes associated with the multiple clusters include one or more of: a number of clusters, a distribution of the multiple clusters, a spread among the multiple clusters, and a spread within a particular cluster of the multiple clusters.
20 . The computer storage media of claim 15 , wherein the objects are classified into multiple clusters using a supervised machine learning model trained on a corpus of labeled data set identifying various forms of marine life.