Articles (2015-Present)
Primary Author Articles
11. Zhang W., R. Kim, S.V. Subramanian. “Crowdsourcing for robust, real-time COVID-19 data”. Nature India, 2020.
10. Zhang W., A.K. Liljedahl, M. Kanevskiy, H.E. Epstein, B.M. Jones, M.T. Jorgenson, and K. Kent. " Transferability of deep learning Mask R-CNN model for automated mapping of ice-wedge polygons in high-resolution satellite and UAV image." Remote Sensing, 2020. [PDF]
9. Zhang W., W. Li, C. Zhang, D. Hanink, Y. Liu, and R. Zhai. "Analyzing horizontal and vertical urban expansions in three East Asian megacities with the SS-coMCRF model". Landscape and Urban Planning, 2018. [PDF]
8. Zhang W., W. Li, C. Zhang, and T. Zhao. "Parallel computing solutions for Markov chain spatial sequential simulation of categorical fields". International Journal of Digital Earth, 2018. [PDF]
7. Zhang W., C. Witharana, A.K. Liljedahl, and M. Kanevskiy. " Deep convolutional neural networks for automated characterization of Arctic ice-wedge polygons in very high spatial resolution aerial imagery." Remote Sensing, 2018. [PDF] [CODE & DATAdrive.google.com/file/d/1BrbQYMWnsWzLKAzYBcpcNMSzJU-A1Yg-/view?usp=sharing]
8. Zhang W., C. Witharana, W. Li, C. Zhang, X. Li, and J. Parent. "Using deep learning to identify geographic objects and estimate their locations from Google Street View images: A case study of utility poles with crossarms." Sensor, 2018. [PDF]
5. Zhang W., W. Li, C. Zhang, and W. Ouimet. "Detecting horizontal and vertical urban growths from medium resolution imagery and their relationships with major socioeconomic factors". International Journal of Remote Sensing, 2017. [PDF]
4. Zhang W., W. Li, C. Zhang, D. Hanink, X. Li, and W. Wang. "Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View". Computers, Environment and Urban Systems, 2017. [PDF]
3. Zhang W., W. Li, C. Zhang, D. Hanink, X. Li, and W. Wang. "Parcel feature data derived from Google Street View images for urban land use classification in Brooklyn, New York City". Data in Brief , 2017. [PDF]
2. Zhang W., W. Li, C. Zhang, and X. Li. "Incorporating Spectral Similarity into Markov Chain Geostatistical Cosimulation for Reducing Smoothing Effect in Land Cover Post-Classification". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016. [PDF]
1. Zhang W., W. Li, and C. Zhang. "Land cover post-classifications by Markov chain geostatistical cosimulation based on pre-classifications by different conventional classifiers". International Journal of Remote Sensing, 2016. [PDF]
Co-author Articles
4. Subramanian, S. V., O. Karlsson, W. Zhang, and R. Kim. “Geo-mapping of COVID-19 Risk Correlates Across Districts and Parliamentary Constituencies in India.” Harvard Data Science Review, 2020.
3. Zhai R., W. Li, C. Zhang, W. Zhang, and W. Wang. “The transiogram as a graphic metric for characterizing the spatial patterns of landscapes”. Landscape Ecology, 2018.
2. Wang W., W. Li, C. Zhang, and W. Zhang. “Improving Object-Based Land Use/Cover Classification from Medium Resolution Imagery by Markov Chain Geostatistical Post-Classification”. Land, 2018. [PDF]
1. Li X., C. Zhang, W. Li, R. Ricard, Q. Meng, W. Zhang. "Assessing Street-Level Urban Greenery Using Google Street View and a Modified Green View Index". Urban Forestry and Urban Greening, 2015. [PDF]
Primary Author Articles
11. Zhang W., R. Kim, S.V. Subramanian. “Crowdsourcing for robust, real-time COVID-19 data”. Nature India, 2020.
10. Zhang W., A.K. Liljedahl, M. Kanevskiy, H.E. Epstein, B.M. Jones, M.T. Jorgenson, and K. Kent. " Transferability of deep learning Mask R-CNN model for automated mapping of ice-wedge polygons in high-resolution satellite and UAV image." Remote Sensing, 2020. [PDF]
9. Zhang W., W. Li, C. Zhang, D. Hanink, Y. Liu, and R. Zhai. "Analyzing horizontal and vertical urban expansions in three East Asian megacities with the SS-coMCRF model". Landscape and Urban Planning, 2018. [PDF]
8. Zhang W., W. Li, C. Zhang, and T. Zhao. "Parallel computing solutions for Markov chain spatial sequential simulation of categorical fields". International Journal of Digital Earth, 2018. [PDF]
7. Zhang W., C. Witharana, A.K. Liljedahl, and M. Kanevskiy. " Deep convolutional neural networks for automated characterization of Arctic ice-wedge polygons in very high spatial resolution aerial imagery." Remote Sensing, 2018. [PDF] [CODE & DATAdrive.google.com/file/d/1BrbQYMWnsWzLKAzYBcpcNMSzJU-A1Yg-/view?usp=sharing]
8. Zhang W., C. Witharana, W. Li, C. Zhang, X. Li, and J. Parent. "Using deep learning to identify geographic objects and estimate their locations from Google Street View images: A case study of utility poles with crossarms." Sensor, 2018. [PDF]
5. Zhang W., W. Li, C. Zhang, and W. Ouimet. "Detecting horizontal and vertical urban growths from medium resolution imagery and their relationships with major socioeconomic factors". International Journal of Remote Sensing, 2017. [PDF]
4. Zhang W., W. Li, C. Zhang, D. Hanink, X. Li, and W. Wang. "Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View". Computers, Environment and Urban Systems, 2017. [PDF]
3. Zhang W., W. Li, C. Zhang, D. Hanink, X. Li, and W. Wang. "Parcel feature data derived from Google Street View images for urban land use classification in Brooklyn, New York City". Data in Brief , 2017. [PDF]
2. Zhang W., W. Li, C. Zhang, and X. Li. "Incorporating Spectral Similarity into Markov Chain Geostatistical Cosimulation for Reducing Smoothing Effect in Land Cover Post-Classification". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016. [PDF]
1. Zhang W., W. Li, and C. Zhang. "Land cover post-classifications by Markov chain geostatistical cosimulation based on pre-classifications by different conventional classifiers". International Journal of Remote Sensing, 2016. [PDF]
Co-author Articles
4. Subramanian, S. V., O. Karlsson, W. Zhang, and R. Kim. “Geo-mapping of COVID-19 Risk Correlates Across Districts and Parliamentary Constituencies in India.” Harvard Data Science Review, 2020.
3. Zhai R., W. Li, C. Zhang, W. Zhang, and W. Wang. “The transiogram as a graphic metric for characterizing the spatial patterns of landscapes”. Landscape Ecology, 2018.
2. Wang W., W. Li, C. Zhang, and W. Zhang. “Improving Object-Based Land Use/Cover Classification from Medium Resolution Imagery by Markov Chain Geostatistical Post-Classification”. Land, 2018. [PDF]
1. Li X., C. Zhang, W. Li, R. Ricard, Q. Meng, W. Zhang. "Assessing Street-Level Urban Greenery Using Google Street View and a Modified Green View Index". Urban Forestry and Urban Greening, 2015. [PDF]
Presentations and Talks (2015-Present)
6. Zhang W. “A Geographic Information Science Researcher’s Story”. Bedford High School, February 9th, 2018, Bedford, MA. (Invited)
5. Zhang W., W. Li, C. Zhang, D. Hanink, X. Li, and W. Wang. “Use of Google Street View in urban land use classification”. Paper presented at the Association of American Geographers meeting, April 6th, 2017, Boston, MA.
4. Zhang, W., M. Howser, and Q. Hu. 2016. “Overview of Connecticut Population Projections from 2015 to 2040”. CT DATA Conference, “Counting What Matters: Better Data for Better Policy in CT”, December 9th, 2016, Hartford, CT.
3. Zhang W., W. Li, C. Zhang, and X. Li.2016. "Incorporating Spectral Similarity into Markov Chain Geostatistical Cosimulation for Improving Land Cover Classification". Paper presented at the Association of American Geographers meeting, April 2nd, 2016, San Francisco, CA.
2. Zhang W., W. Li, and C. Zhang. 2015. "Comparison of land cover post-classifications by Markov chain random field cosimulation with different conventional classifiers". The 23rd International Conference on Geoinformatics, June 19-21, 2015, Wuhan, China.
1. Zhang W., W. Li, and C. Zhang.2015. "A Comparison of Markov chain random field (MCRF) Cosimulation for improving land cover pre-classiifcation". Paper presented at the Association of American Geographers meeting, April 24th, 2015, Chicago, IL.
6. Zhang W. “A Geographic Information Science Researcher’s Story”. Bedford High School, February 9th, 2018, Bedford, MA. (Invited)
5. Zhang W., W. Li, C. Zhang, D. Hanink, X. Li, and W. Wang. “Use of Google Street View in urban land use classification”. Paper presented at the Association of American Geographers meeting, April 6th, 2017, Boston, MA.
4. Zhang, W., M. Howser, and Q. Hu. 2016. “Overview of Connecticut Population Projections from 2015 to 2040”. CT DATA Conference, “Counting What Matters: Better Data for Better Policy in CT”, December 9th, 2016, Hartford, CT.
3. Zhang W., W. Li, C. Zhang, and X. Li.2016. "Incorporating Spectral Similarity into Markov Chain Geostatistical Cosimulation for Improving Land Cover Classification". Paper presented at the Association of American Geographers meeting, April 2nd, 2016, San Francisco, CA.
2. Zhang W., W. Li, and C. Zhang. 2015. "Comparison of land cover post-classifications by Markov chain random field cosimulation with different conventional classifiers". The 23rd International Conference on Geoinformatics, June 19-21, 2015, Wuhan, China.
1. Zhang W., W. Li, and C. Zhang.2015. "A Comparison of Markov chain random field (MCRF) Cosimulation for improving land cover pre-classiifcation". Paper presented at the Association of American Geographers meeting, April 24th, 2015, Chicago, IL.
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