This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, ...This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, MotionSense, and PAMAP2—to develop a generalized MLP model for classifying six human activities. Performance analysis of the fused model for each dataset reveals accuracy rates of 95.83 for WISDM, 97 for DaLiAc, 94.65 for MotionSense, and 98.54 for PAMAP2. A comparative evaluation was conducted between the fused MLP model and the individual dataset models, with the latter tested on separate validation sets. The results indicate that the MLP model, trained on the fused dataset, exhibits superior performance relative to the models trained on individual datasets. This finding suggests that multisource data fusion significantly enhances the generalization and accuracy of HAR systems. The improved performance underscores the potential of integrating diverse data sources to create more robust and comprehensive models for activity recognition.展开更多
Despite its essential importance to various spatial agriculture and environmental applications,the information on actual cropland area and its geographical distribution remain highly uncertain over Africa among remote...Despite its essential importance to various spatial agriculture and environmental applications,the information on actual cropland area and its geographical distribution remain highly uncertain over Africa among remote-sensing products.Each of the African regions has its unique physical and environmental limiting factors to accurate cropland mapping,which leads to high spatial discre-pancies among remote sensing cropland products.Since no dataset could cope with all limitations,multiple datasets initially derived from various remote sensing sensors and classification techniques must be integrated into a more accurate cropland product than individual layers.Here,in the current study,four cropland products,produced initially from multiple sensors(e.g.Landsat-8 OLI,Sentinel-2 MSI,and PROBA-V)to cover the period(2015-2017),were integrated based on their cropland mapping accuracy to build a more accurate cropland layer.The four cropland layers’accuracy was assessed at Agro-ecological zones units via an inten-sive reference dataset(17,592 samples).The most accurate crop-land layer was then identified for each zone to construct the final cropland mask at 30 m resolution for the nominal year of 2016 over Africa.As a result,the new layer was produced in higher cropland mapping accuracy(overall accuracy=91.64%and cropland’s F-score=0.75).The layer mapped the African cropland area as 282 Mha(9.38%of the Continent area).Compared to earlier crop-land synergy layers,the constructed cropland mask showed a considerable improvement in its spatial resolution(30 m instead of 250 m),mapping quality,and closeness to official statistics(R^(2)=0.853 and RMSE=2.85 Mha).The final layer can be down-loaded as described under the“Data Availability Statement”section.展开更多
基金supported by the Royal Golden Jubilee(RGJ)Ph.D.Programme(Grant No.PHD/0079/2561)through the National Research Council of Thailand(NRCT)and Thailand Research Fund(TRF).
文摘This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, MotionSense, and PAMAP2—to develop a generalized MLP model for classifying six human activities. Performance analysis of the fused model for each dataset reveals accuracy rates of 95.83 for WISDM, 97 for DaLiAc, 94.65 for MotionSense, and 98.54 for PAMAP2. A comparative evaluation was conducted between the fused MLP model and the individual dataset models, with the latter tested on separate validation sets. The results indicate that the MLP model, trained on the fused dataset, exhibits superior performance relative to the models trained on individual datasets. This finding suggests that multisource data fusion significantly enhances the generalization and accuracy of HAR systems. The improved performance underscores the potential of integrating diverse data sources to create more robust and comprehensive models for activity recognition.
基金was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19030201]National Natural Science Foundation of China[41861144019 and 41561144013].
文摘Despite its essential importance to various spatial agriculture and environmental applications,the information on actual cropland area and its geographical distribution remain highly uncertain over Africa among remote-sensing products.Each of the African regions has its unique physical and environmental limiting factors to accurate cropland mapping,which leads to high spatial discre-pancies among remote sensing cropland products.Since no dataset could cope with all limitations,multiple datasets initially derived from various remote sensing sensors and classification techniques must be integrated into a more accurate cropland product than individual layers.Here,in the current study,four cropland products,produced initially from multiple sensors(e.g.Landsat-8 OLI,Sentinel-2 MSI,and PROBA-V)to cover the period(2015-2017),were integrated based on their cropland mapping accuracy to build a more accurate cropland layer.The four cropland layers’accuracy was assessed at Agro-ecological zones units via an inten-sive reference dataset(17,592 samples).The most accurate crop-land layer was then identified for each zone to construct the final cropland mask at 30 m resolution for the nominal year of 2016 over Africa.As a result,the new layer was produced in higher cropland mapping accuracy(overall accuracy=91.64%and cropland’s F-score=0.75).The layer mapped the African cropland area as 282 Mha(9.38%of the Continent area).Compared to earlier crop-land synergy layers,the constructed cropland mask showed a considerable improvement in its spatial resolution(30 m instead of 250 m),mapping quality,and closeness to official statistics(R^(2)=0.853 and RMSE=2.85 Mha).The final layer can be down-loaded as described under the“Data Availability Statement”section.
