Louisa Diggs

4 Seguidores

machine learning machine learning methods geospatial data training data land cover analysis/ habitat change. radiant earth foundation disease mapping agriculture automated change detection building footprints derived from satellite imagery population estimation remotely-sensed data analysis modeling research design evaluation program planning disease surveillance yellow fever viral spillovers poxvirus spatiotemporal risk ecological niche models democratic republic of congo monkeypox lymphatic filariasis podoconiosis neglected tropical disease(s) surveillance geostatistical methods open-source remote sensing data and tools earth observation map clark university land cover mapping spatial data labels applications openstreetmap geodiversity data errors smallholder farmers classification fire food systems radiant mlhub big data data science ai land use mapping crowdsourcing land use data quantifying errors uncertain data informal settlements cadastral mapping rastervision land remote sensing policy remote sensing active sensors sar very high resolution satellite providers commercial weather satellite operators commercial visible eo satellites radar satellites commercial space operators

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IMED 2018: The use of remote sensing, geostatistical and machine learning methods in neglected tropical diseases surveillance — the case of podoconiosis and lymphatic filariasis.

Hace 5 años • 174 Visualizaciones

Using Active Learning to Quantify how Training Data Errors Impact Classification Accuracy over Smallholder-Dominated Agricultural Systems

Hace 4 años • 26 Visualizaciones

Workshop: Quantifying Error in Training Data for Mapping and Monitoring the Earth System

Hace 4 años • 105 Visualizaciones

Louisa Diggs

Presentaciones

IMED 2018: Innovations and Challenges in the Use of Open-source Remote Sensing Data and Tools.

IMED 2018: Predicting spatiotemporal risk of yellow fever using a machine learning approach.

IMED 2018: Mapping Monkeypox risk in the Congo Basin using Remote Sensing and Ecological Niche Models.

IMED 2018: An intro to Remote Sensing and Machine Learning

IMED 2018: Modeled Population Estimates from Satellite Imagery and Microcensus Data

IMED 2018: Landcover/habitat

IMED 2018: Predicting the environmental suitability of podoconiosis in Ethiopia

IMED 2018: The use of remote sensing, geostatistical and machine learning methods in neglected tropical diseases surveillance — the case of podoconiosis and lymphatic filariasis.

Crowdsourcing Land Cover and Land Use Data: Experiences from IIASA

Mapping Smallholder Yields Using Micro-Satellite Data

Measuring the impact of label noise on semantic segmentation using rastervision

Sources of Map Error in Public Health Activities and Operations Research

Informal Settlements and Cadastral Mapping

Assessing Land Cover Change using Uncertain Data

A Random Walk of Issues Related to Training Data and Land Cover Mapping

Challenges to Large Scale Mapping: Can Data Geometry Help?

Cropped Field Boundaries, Food Systems, & Fire

Generating Training Data from Noisy Measrements

Machine Learning for Better Maps

Using Active Learning to Quantify how Training Data Errors Impact Classification Accuracy over Smallholder-Dominated Agricultural Systems

Workshop: Quantifying Error in Training Data for Mapping and Monitoring the Earth System