公开(公告)号：US20180336452A1

公开(公告)日：2018-11-22

申请号：US15601739

申请日：2017-05-22

Applicant: SAP SE

Inventor： Vadim Tschernezki ,  Oliver Blum ,  Hinnerk Gildhoff ,  Michèle Wyss ,  Bjoern Deiseroth ,  Wenzel Svojanovsky

IPC: G06N3/04 ,  G06N3/08

Abstract: The present disclosure involves systems, software, and computer implemented methods for predicting wildfires on the basis of biophysical indicators and spatiotemporal properties. A method includes receiving a request for a wildfire prediction for at least one geographical area. At least one biophysical indicator is identified. Each biophysical indicator provides biophysical data for the at least one geographical area. The at least one biophysical indicator is provided to a long short term memory (LSTM) network. The LSTM network includes a convolutional neural network (CNN) for each of multiple LSTM units. Each LSTM unit and each CNN are associated with a historical time period in a time series. The LSTM is used to generate at least one prediction for wildfire risk for the at least one geographical area for an upcoming time period. The at least one prediction is provided responsive to the request.

公开(公告)号：US10990874B2

公开(公告)日：2021-04-27

申请号：US15601704

申请日：2017-05-22

Applicant: SAP SE

Inventor： Vadim Tschernezki ,  Oliver Blum ,  Hinnerk Gildhoff ,  Michèle Wyss ,  Bjoern Deiseroth ,  Wenzel Svojanovsky

IPC: G06N3/04 ,  G06N3/08

Abstract: Systems, software, and computer implemented methods can be used to predict wildfires based on biophysical and spatiotemporal data. A method includes receiving a request for a wildfire prediction for at least one geographical area. At least one biophysical indicator is identified. Each biophysical indicator provides biophysical data for the at least one geographical area. The at least one biophysical indicator is provided to a convolutional neural network (CNN). The CNN is trained using ground truth data that includes historical information about wildfires for at least one ground truth geographical area. The CNN is used to generate at least one prediction for wildfire risk for the at least one geographical area. The at least one prediction is provided responsive to the request.

公开(公告)号：US11275989B2

公开(公告)日：2022-03-15

申请号：US15601739

申请日：2017-05-22

Applicant: SAP SE

Inventor： Vadim Tschernezki ,  Oliver Blum ,  Hinnerk Gildhoff ,  Michèle Wyss ,  Bjoern Deiseroth ,  Wenzel Svojanovsky

IPC: G06N3/04 ,  G06N3/08

Abstract: The present disclosure involves systems, software, and computer implemented methods for predicting wildfires on the basis of biophysical indicators and spatiotemporal properties. A method includes receiving a request for a wildfire prediction for at least one geographical area. At least one biophysical indicator is identified. Each biophysical indicator provides biophysical data for the at least one geographical area. The at least one biophysical indicator is provided to a long short term memory (LSTM) network. The LSTM network includes a convolutional neural network (CNN) for each of multiple LSTM units. Each LSTM unit and each CNN are associated with a historical time period in a time series. The LSTM is used to generate at least one prediction for wildfire risk for the at least one geographical area for an upcoming time period. The at least one prediction is provided responsive to the request.

公开(公告)号：US20180336460A1

公开(公告)日：2018-11-22

申请号：US15601704

申请日：2017-05-22

Applicant: SAP SE

Inventor： Vadim Tschemezki ,  Oliver Blum ,  Hinnerk Gildhoff ,  Michèle Wyss ,  Bjoern Deiseroth ,  Wenzel Svojanovsky

IPC: G06N3/08 ,  G06N3/04

Abstract: The present disclosure involves systems, software, and computer implemented methods for predicting wildfires on the basis of biophysical indicators and spatiotemporal properties. A method includes receiving a request for a wildfire prediction for at least one geographical area. At least one biophysical indicator is identified. Each biophysical indicator provides biophysical data for the at least one geographical area. The at least one biophysical indicator is provided to a convolutional neural network (CNN). The CNN is trained using ground truth data that includes historical information about wildfires for at least one ground truth geographical area. The CNN is used to generate at least one prediction for wildfire risk for the at least one geographical area. The at least one prediction is provided responsive to the request.