Abstract: This framework provides scalable land use characterization using Points of Interest (POIs) and non-POI geographic features. Leveraging open-access POI data and hierarchical spatial structures, it generates high-dimensional embeddings that capture spatial and semantic characteristics of land use for areas of interest (AOIs). An OSM-tag-based representation harmonizes diverse data sources, while a neural network language model produces embeddings optimized for multi-scale land use classification across geographic regions. Supervised classification models validate the robustness of AOI embeddings, revealing variations in semantic salience for different land use types. Results demonstrate that combining POIs with non-POI features and tailoring spatial and semantic granularities enhance land use characterization. Future directions include augmenting data and integrating temporal dynamics to improve representational accuracy and capture land use patterns more effectively.

Abstract: Techniques are described that include accessing information about points of interest and images of scenes within the area of interest; encoding the information about each scene image as a respective scene-image vector; encoding the information about each point of interest as a respective point-of-interest vector; constructing a joint semantic graph having nodes and edges by (i) attributing to each node a respective point-of-interest vector or a respective scene-image vector, (ii) determining semantic distances between pairs of point-of-interest vectors, pairs of scene-image vectors, and pairs formed from a point-of-interest vector and a scene-image vector, and (iii) connecting each node with respective edges to a predetermined number of nearest-neighbor nodes having respective vectors with lowest semantic distances to each other. The constructed joint semantic graph can be used to enrich and/or clean the information about the points of interest and/or the images of scenes within the area of interest.

Abstract: A prediction system harvests geo-tagged ground-level images through one or more algorithms. The system receives point of interest data representing structures or events and tags the geo-tagged ground-level images with a probability describing a classification. The system tags point of interest data with a hierarchical genre classification and encodes the tagged geo-tagged ground-level images as vectors to form nodes and edges in a proximity graph. The system encodes tagged points of interest data as similarity vectors to render more nodes and more edges on the proximity graph associated with the tagged geo-tagged ground-level images nodes by calculated semantic distances. The system splits the proximity graph into a training subgraph and a testing subgraph and trains a neural network by aggregating and sampling information from neighboring nodes within the training subgraph graph and validates through the testing subgraph. Training ends when a loss measurement is below a threshold.

Abstract: A prediction system harvests geo-tagged ground-level images through one or more algorithms. The system receives point of interest data representing structures or events and tags the geo-tagged ground-level images with a probability describing a classification. The system tags point of interest data with a hierarchical genre classification and encodes the tagged geo-tagged ground-level images as vectors to form nodes and edges in a proximity graph. The system encodes tagged points of interest data as similarity vectors to render more nodes and more edges on the proximity graph associated with the tagged geo-tagged ground-level images nodes by calculated semantic distances. The system splits the proximity graph into a training subgraph and a testing subgraph and trains a neural network by aggregating and sampling information from neighboring nodes within the training subgraph graph and validates through the testing subgraph. Training ends when a loss measurement is below a threshold.

Abstract: The present invention is directed to a palatable, hard chewable composition comprising a therapeutically effective amount of a veterinary acceptable isoxazoline, a stabilized macrocyclic lactone, an acceptable salt form of pyrantel, at least one natural animal based palatant, and at least one veterinary acceptable excipient; and methods for treating or preventing a parasitic infection or infestation in an animal in need thereof with said composition.

Abstract: The present invention is directed to a palatable, hard chewable composition comprising a therapeutically effective amount of a veterinary acceptable isoxazoline, a stabilized macrocyclic lactone, an acceptable salt form of pyrantel, at least one natural animal based palatant, and at least one veterinary acceptable excipient; and methods for treating or preventing a parasitic infection or infestation in an animal in need thereof with said composition.

Abstract: An apparatus for coding of an anchor point is disclosed. The apparatus includes a single point anchor and a coded element associated with the single point anchor. The coded element includes an indicia that is indicative of a use of the single point anchor with a function. A method for coding of an anchor point in a structure is also disclosed. The method includes associating a coded element with a single point anchor, where the coded element includes an indicia that is indicative of a use of the single point anchor with a function. The single point anchor and the coded element are disposed in the structure.