158 research outputs found
Evaluating Student Perceptions of Course Delivery Platforms
In this paper we evaluate effectiveness of course delivery mode on three dimensions: values, networking opportunities and learning. While students and their future employers are two important customers for the business program, we focus on the perception of students regarding the effectiveness of course delivery mode on program performance. The three dimensions are evaluated based on a questionnaire survey administered to business program students at several universities. We present the results of statistical analysis and draw conclusions based on the results.
Ants constructing rule-based classifiers.
Classifiers; Data; Data mining; Studies;
Recent Ice Trends in Swiss Mountain Lakes: 20-year Analysis of MODIS Imagery
Depleting lake ice is a climate change indicator, just like sea-level rise or glacial retreat. Monitoring Lake Ice Phenology (LIP) is useful because long-term freezing and thawing patterns serve as sentinels to understand regional and global climate change. We report a study for the Oberengadin region of Switzerland, where several small- and medium-sized mountain lakes are located. We observe the LIP events, such as freeze-up, break-up and ice cover duration, across two decades (2000ā2020) from optical satellite images. We analyse the time series of MODIS imagery by estimating spatially resolved maps of lake ice for these Alpine lakes with supervised machine learning. To train the classifier we rely on reference data annotated manually based on webcam images. From the ice maps, we derive long-term LIP trends. Since the webcam data are only available for two winters, we cross-check our results against the operational MODIS and VIIRS snow products. We find a change in complete freeze duration of ā0.76 and ā0.89 days per annum for lakes Sils and Silvaplana, respectively. Furthermore, we observe plausible correlations of the LIP trends with climate data measured at nearby meteorological stations. We notice that mean winter air temperature has a negative correlation with the freeze duration and break-up events and a positive correlation with the freeze-up events. Additionally, we observe a strong negative correlation of sunshine during the winter months with the freeze duration and break-up events
Recent Ice Trends in Swiss Mountain Lakes: 20-year Analysis of MODIS Imagery
Depleting lake ice is a climate change indicator, just like sea-level rise or
glacial retreat. Monitoring Lake Ice Phenology (LIP) is useful because
long-term freezing and thawing patterns serve as sentinels to understand
regional and global climate change. We report a study for the Oberengadin
region of Switzerland, where several small- and medium-sized mountain lakes are
located. We observe the LIP events, such as freeze-up, break-up and ice cover
duration, across two decades (2000-2020) from optical satellite images. We
analyse the time series of MODIS imagery by estimating spatially resolved maps
of lake ice for these Alpine lakes with supervised machine learning. To train
the classifier we rely on reference data annotated manually based on webcam
images. From the ice maps, we derive long-term LIP trends. Since the webcam
data are only available for two winters, we cross-check our results against the
operational MODIS and VIIRS snow products. We find a change in complete freeze
duration of -0.76 and -0.89 days per annum for lakes Sils and Silvaplana,
respectively. Furthermore, we observe plausible correlations of the LIP trends
with climate data measured at nearby meteorological stations. We notice that
mean winter air temperature has a negative correlation with the freeze duration
and break-up events and a positive correlation with the freeze-up events.
Additionally, we observe a strong negative correlation of sunshine during the
winter months with the freeze duration and break-up events.Comment: accepted for PFG Journal of Photogrammetry, Remote Sensing and
Geoinformation Scienc
Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring
Fusing satellite imagery acquired with different sensors has been a
long-standing challenge of Earth observation, particularly across different
modalities such as optical and Synthetic Aperture Radar (SAR) images. Here, we
explore the joint analysis of imagery from different sensors in the light of
representation learning: we propose to learn a joint embedding of multiple
satellite sensors within a deep neural network. Our application problem is the
monitoring of lake ice on Alpine lakes. To reach the temporal resolution
requirement of the Swiss Global Climate Observing System (GCOS) office, we
combine three image sources: Sentinel-1 SAR (S1-SAR), Terra MODIS, and
Suomi-NPP VIIRS. The large gaps between the optical and SAR domains and between
the sensor resolutions make this a challenging instance of the sensor fusion
problem. Our approach can be classified as a late fusion that is learned in a
data-driven manner. The proposed network architecture has separate encoding
branches for each image sensor, which feed into a single latent embedding.
I.e., a common feature representation shared by all inputs, such that
subsequent processing steps deliver comparable output irrespective of which
sort of input image was used. By fusing satellite data, we map lake ice at a
temporal resolution of < 1.5 days. The network produces spatially explicit lake
ice maps with pixel-wise accuracies > 91% (respectively, mIoU scores > 60%) and
generalises well across different lakes and winters. Moreover, it sets a new
state-of-the-art for determining the important ice-on and ice-off dates for the
target lakes, in many cases meeting the GCOS requirement
Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring
Fusing satellite imagery acquired with different sensors has been a long-standing challenge of Earth observation, particularly across different modalities such as optical and synthetic aperture radar (SAR) images. Here, we explore the joint analysis of imagery from different sensors in the light of representation learning: we propose to learn a joint embedding of multiple satellite sensors within a deep neural network. Our application problem is the monitoring of lake ice on Alpine lakes. To reach the temporal resolution requirement of the Swiss Global Climate Observing System (GCOS) office, we combine three image sources: Sentinel-1 SAR (S1-SAR), Terra moderate resolution imaging spectroradiometer (MODIS), and Suomi-NPP visible infrared imaging radiometer suite (VIIRS). The large gaps between the optical and SAR domains and between the sensor resolutions make this a challenging instance of the sensor fusion problem. Our approach can be classified as a late fusion that is learned in a data-driven manner. The proposed network architecture has separate encoding branches for each image sensor, which feed into a single latent embedding, i.e., a common feature representation shared by all inputs, such that subsequent processing steps deliver comparable output irrespective of which sort of input image was used. By fusing satellite data, we map lake ice at a temporal resolution of 91% [respectively, mean per-class Intersection-over-Union (mIoU) scores >60%] and generalizes well across different lakes and winters. Moreover, it sets a new state-of-the-art for determining the important ice-on and ice-off dates for the target lakes, in many cases meeting the GCOS requirement
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