Abstract: A system for automated geospatial image analysis comprising a deep learning model that receives orthorectified geospatial images, pre-labeled to demarcate objects of interest. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to a convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives orthorectified geospatial images from one or more geospatial image caches. Using a multi-scale sliding window submodule, image analysis software scans geospatial images, detects objects present and geospatially locates them.

Abstract: A system for automated geospatial image analysis comprising a deep learning model that receives orthorectified geospatial images, pre-labeled to demarcate objects of interest. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to a convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives orthorectified geospatial images from one or more geospatial image caches. Using a multi-scale sliding window submodule, image analysis software scans geospatial images, detects objects present and geospatially locates them.

Abstract: A system for automated geospatial image analysis comprising a deep learning model that receives orthorectified geospatial images, pre-labeled to demarcate objects of interest. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to a convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives orthorectified geospatial images from one or more geospatial image caches. Using a multi-scale sliding window submodule, image analysis software scans geospatial images, detects objects present and geospatially locates them.

Abstract: A system for automated geospatial image analysis comprising a deep learning model that receives orthorectified geospatial images, pre-labeled to demarcate objects of interest. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to a convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives orthorectified geospatial images from one or more geospatial image caches. Using a multi-scale sliding window submodule, image analysis software scans geospatial images, detects objects present and geospatially locates them.

Abstract: A system for automated geospatial image analysis comprising a deep learning model module and a convolutional neural network serving as an automated image analysis software module. The deep learning module receives a plurality of orthorectified geospatial images, pre-labeled to demarcate objects of interest, and optimized for the purpose of training the neural network of the image analysis software module. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to the convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives a plurality of orthorectified geospatial images from one or more geospatial image caches. Using multi-scale sliding window submodule, image analysis modules scan geospatial images, detect objects present and locate them on the geographical latitude-longitude system.

Abstract: A system for automated geospatial image analysis comprising a deep learning model module and a convolutional neural network serving as an automated image analysis software module. The deep learning module receives a plurality of orthorectified geospatial images, pre-labeled to demarcate objects of interest, and optimized for the purpose of training the neural network of the image analysis software module. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to the convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives a plurality of orthorectified geospatial images from one or more geospatial image caches. Using multi-scale sliding window submodule, image analysis modules scan geospatial images, detect objects present and locate them on the geographical latitude-longitude system.

Abstract: A system and a method for monitoring and inspecting food safety is disclosed. The system adopts insert and use concept that only requires an initial push on a button to begin its function. The system provides visual alert for different conditions if food products are in unsafe status. The system is pre-calibrated during manufacture without complicated or multi-step calibration or recalibration procedures during application. The system relies on modern surface-mount microprocessor technology that enables long-term calibration stability along with very low power consumption for extended battery life.

Abstract: A system for automated geospatial image analysis comprising a deep learning model module and a convolutional neural network serving as an automated image analysis software module. The deep learning module receives a plurality of orthorectified geospatial images, pre-labeled to demarcate objects of interest, and optimized for the purpose of training the neural network of the image analysis software module. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to the convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives a plurality of orthorectified geospatial images from one or more geospatial image caches. Using multi-scale sliding window submodule, image analysis modules scan geospatial images, detect objects present and locate them on the geographical latitude-longitude system.

Abstract: A system for automated geospatial image analysis comprising a deep learning model module and a convolutional neural network serving as an automated image analysis software module. The deep learning module receives a plurality of orthorectified geospatial images, pre-labeled to demarcate objects of interest, and optimized for the purpose of training the neural network of the image analysis software module. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to the convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives a plurality of orthorectified geospatial images from one or more geospatial image caches. Using multi-scale sliding window submodule, image analysis modules scan geospatial images, detect objects present and locate them on the geographical latitude-longitude system.

Abstract: A system and a method for monitoring and inspecting food safety is disclosed. The system adopts insert and use concept that only requires an initial push on a button to begin its function. The system provides visual alert for different conditions if food products are in unsafe status. The system is pre-calibrated during manufacture without complicated or multi-step calibration or recalibration procedures during application. The system relies on modern surface-mount microprocessor technology that enables long-term calibration stability along with very low power consumption for extended battery life.

Abstract: A system and a method for monitoring and inspecting food safety is disclosed. The system adopts insert and use concept that only requires an initial push on a button to begin its function. The system provides visual alert for different conditions if food products are in unsafe status. The system is pre-calibrated during manufacture without complicated or multi-step calibration or recalibration procedures during application. The system relies on modern surface-mount microprocessor technology that enables long-term calibration stability along with very low power consumption for extended battery life.

Abstract: A system and a method for monitoring and inspecting food safety is disclosed. The system adopts insert and use concept that only requires an initial push on a button to begin its function. The system provides visual alert for different conditions if food products are in unsafe status. The system is pre-calibrated during manufacture without complicated or multi-step calibration or recalibration procedures during application. The system relies on modern surface-mount microprocessor technology that enables long-term calibration stability along with very low power consumption for extended battery life.

Abstract: A system and a method for monitoring and inspecting food safety is disclosed. The system adopts insert and use concept that only requires an initial push on a button to begin its function. The system provides visual alert for different conditions if food products are in unsafe status. The system is pre-calibrated during manufacture without complicated or multi-step calibration or recalibration procedures during application. The system relies on modern surface-mount microprocessor technology that enables long-term calibration stability along with very low power consumption for extended battery life.

Abstract: A system and a method for monitoring and inspecting food safety is disclosed. The system adopts insert and use concept that only requires an initial push on a button to begin its function. The system provides visual alert for different conditions if food products are in unsafe status. The system is pre-calibrated during manufacture without complicated or multi-step calibration or recalibration procedures during application. The system relies on modern surface-mount microprocessor technology that enables long-term calibration stability along with very low power consumption for extended battery life.

Abstract: A system and a method for monitoring and inspecting food safety is disclosed. The system adopts insert and use concept that only requires an initial push on a button to begin its function. The system provides visual alert for different conditions if food products are in unsafe status. The system is pre-calibrated during manufacture without complicated or multi-step calibration or recalibration procedures during application. The system relies on modern surface-mount microprocessor technology that enables long-term calibration stability along with very low power consumption for extended battery life.