Refined searching based on detected object configurations is provided by training a machine learning model to identify non-naturally occurring object configurations, acquiring images of an initial search area based on scanning it using a camera-equipped autonomous aerial vehicle operating in accordance with an initial automated flight plan defining the initial search area, analyzing the acquired images using the trained machine learning model and identifying that an object configuration is a non-naturally occurring object configuration, then based on identifying the non-naturally occurring object configuration, refining the initial automated flight plan to obtain a modified automated flight plan defining a different search area as compared to the initial search area, and initiating autonomous aerial scanning of the different search area in accordance with the modified automated flight plan.

A method for flood disaster monitoring and disaster analysis based on vision transformer is provided. It includes: step (1), constructing a bi-temporal image change detection model based on vision transformer; step (2), selecting bi-temporal remote sensing images to make flood disaster labels; and step (3), performing flood monitoring and disaster analysis according to the bi-temporal image change detection model constructed in the step (1). In combination with the bi-temporal image change detection model based on an advanced vision transformer in deep learning and radar data which is not affected by time and weather and has strong penetration ability, data when floods occur can be obtained and recognition accuracy is improved.

A method for flood disaster monitoring and disaster analysis based on vision transformer is provided. It includes: step (1), constructing a bi-temporal image change detection model based on vision transformer; step (2), selecting bi-temporal remote sensing images to make flood disaster labels; and step (3), performing flood monitoring and disaster analysis according to the bi-temporal image change detection model constructed in the step (1). In combination with the bi-temporal image change detection model based on an advanced vision transformer in deep learning and radar data which is not affected by time and weather and has strong penetration ability, data when floods occur can be obtained and recognition accuracy is improved.

A landslide recognition method based on Laplacian pyramid remote sensing image fusion includes: performing original remote sensing image reconstruction based on extracted local features and global features of remote sensing images through a Laplacian pyramid fusion module to generate a fused image, constructing a deep learning semantic segmentation model through a semantic segmentation network, labeling the fused image to obtain a dataset of landslide disaster label map, and training the deep learning semantic segmentation model by the dataset, and then storing when a loss curve is fitted and a landslide recognition accuracy of remote sensing image of the deep learning semantics segmentation model meets a requirement by modifying a structure of the semantic segmentation network and adjusting parameters of the deep learning semantics segmentation model. Combined with the image fusion model based on Laplacian pyramid, the method can provide effective decision-making basis for prevention and mitigation of landslide disasters.

The present invention discloses a method for recognizing a seawater polluted area based on a high-resolution remote sensing image and a device and belongs to the field of digital image processing. According to the method, firstly, automatic sea and land classification is performed on a remote sensing image by using a supervised learning algorithm, a classification result may reach a higher precision level by processized iterative clustering, and meanwhile, compared with an existing analysis and classification method for a sea and land boundary, the algorithm is less in calculation; and then, a chlorophyll-associated normalized difference vegetation index, a brightness-associated normalized difference water shadow index, a segmentation-based image interpretation thought and a human visual saliency based mechanism in remote sensing interpretation are combined by virtue of a chlorophyll concentration difference of a seawater polluted area and surrounding seawater and a brightness difference of pollutant shadows, and the seawater polluted area is extracted by threshold segmentation, an area where the water quality is good and a heavily polluted area are respectively extracted, and then, a pollution transition area is further extracted. The method disclosed by the present invention provides convenience and an accurate reference for prevention and control of marine pollution.

A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

The present disclosure is directed to a processing system with a virtualized graphics processor for highly parallel processing of graphics tasks as well as other computing tasks. The processing system includes a central processing unit (CPU) configured with a virtualization stack which includes a graphics processing unit (GPU) having hundreds to thousands of GPU cores virtualized into virtual machines (VMs). The GPU cores are loaded with low-level programming routines for graphics tasks. Different GPUs are loaded with different types of programming routines based on their respective dedicated graphics tasks. The cores are segmented into VMs based on the graphics task. By utilizing virtualized GPUs, highly parallel processing of graphics tasks can be achieved.

A plurality of risk relationship sensors, including at least one image capturing sensor (e.g., a camera), may each include an environment characteristic detection element, a power source, and a communication device to transmit data associated with risk relationship sensor data at a site. A risk relationship data store may contain electronic records associated with prior risk relationship events at other sites along with risk relationship sensor location data for those sites. An enterprise analytics platform may automatically analyze the electronic records in the risk relationship data store to create a predictive analytics algorithm. The data associated with potential risk relationship sensor data at the site may then be automatically analyzed, in substantially real-time, using the predictive analytics algorithm, and a result of the analysis may then be transmitted (e.g., to a party associated with the site).

Embodiments of the present disclosure provide a method and apparatus for generating information. A method may include: determining, according to received positioning request information, visiting information for a target area of interest, the visiting information including location information of at least one visiting point; determining, according to the location information of the at least one visiting point, a visiting point distribution map including the at least one visiting point; performing grid division on the visiting point distribution map, to obtain a first grid map including at least one grid; and generating, based on the first grid map, outline information for the target area of interest.

A method comprises receiving an area of interest (AOI) selection. The method further comprises accessing an AOI device location data for the AOI, the AOI device location data indicating locations of devices over time received within the AOI. The AOI device location data is filtered to only include the device location data that match one or more characteristics. A proximity zone is determined for the for the AOI that includes the area of the AOI. A zone device location data for the proximity zone is determined, which indicates locations of devices over time reported within the proximity zone. The method further comprises normalizing the filtered AOI device location data by computing a ratio of the filtered AOI device location data and the zone device location data to generate an AOI user estimate, and transmitting the AOI user estimate to a client device of a requestor.