施孝謙

  San Diego, CA, USA

專長於地理資訊系統GIS、遙感探測Remote Sensing、時空分析。研究興趣是土地利用變遷、都市遙測及人口變遷。運用空間資料分析、數位影像處理及機器學習於研究項目。

    

工作經歷


大學助教

加州聖地牙哥州立大學

八月 2013 - Present
Taipei, Taiwan

運用遙感探測、地理資訊系統及時空分析於學術研究:
一、為大數據之人口、社會經濟及遙測影像資料以視覺化及統計方式建立時空趨勢,並以此趨勢建立預測模型。
二、實驗機器學習於衛星影像以研究土地利用分類之可行性。
三、管理空間關聯式資料庫,包含PostgreSQL及ArcGIS geodatabase。
四、開發以時間序列Landsat衛星影像為主之自動偵測都市化演算法。
實驗課程教學:
一、教授遙測軟體及程式語言,包含ENVI、Erdas Imagine、TerrSet、eCognition、python和R。
二、指導及協助學生在遙測及空間分析相關計畫。

空間影像分析員

國立台灣師範大學—福衛二號影像加值中心

六月 2008 - 六月 2012
Taipei, Taiwan

1. 影像前處理,透過福衛二號影像判釋災害影響地區。
2. 透過空間分析探討台灣社會、經濟、人口分布。
3. 運用福衛二號影像研究台灣土地利用變遷分析。

學歷


加州大學聖塔芭芭拉分校 聖地牙哥州立大學 博士

地理

2015 - 2020

聖地牙哥州立大學 碩士

地理資訊科學

2013 - 2015

國立台灣師範大學 學士

地理

2007 - 2011

技能

軟體


  • ArcGIS
  • QGIS
  • ENVI
  • Erdas Imagine
  • TerrSet

程式語言


  • Python
  • R
  • PostgreSQL
  • Java

專長領域


  • 空間資訊(GIS)
  • 遙感探測
  • 時空模型
  • 機器學習
  • 人口學
  • 時間序列分析

語言


  • 中文
  • 英文

研究論文

Shih, H., An, L., Weeks, J. R., & Stow, D. A. (in preparation). Addressing spatial autocorrelation in space-time analysis: A case study of Southeastern Ghananian women’s body mass index.

Shih, H., Weeks, J. R., Goulias, K.G., & Stow, D. A. (submitted). The relative timing of population growth and land use change – A case study of north Taiwan from 1990 to 2015. Annals of the American Association of Geographers.

Shih, H., Stow, D. A., Roberts, D. A., Weeks, J. R., & Goulias, K. G. (submitted). From land cover to land use – machine learning classification and feature selection on Landsat imagery. International Journal of Remote Sensing.

Shih, H., Stow, D. A., Tsai, Y. M., & Roberts, D. A. (2020). Estimating the starting time and identifying the type of urbanization based on dense time series of Landsat-derived Vegetation-Impervious-Soil (V-I-S) maps – A case study of north Taiwan from 1990 to 2015. International Journal of Applied Earth Observations and Geoinformation.

Shih, H. (2020). The relative timing of human migration and land cover and land use change — an evaluation of northern Taiwan from 1990 to 2015. (Doctoral dissertation, University of California, Santa Barbara – San Diego State University).

Shih, H., Stow, D. A., & Tsai, Y. H. (2019). Guidance on and comparison of machine learning classifiers for Landsat-based land cover and land use mapping. International journal of remote sensing, 40(4), 1248-1274.

Toure, S., Stow, D., Shih, H., Weeks, J., Lopez-Carr, D. (2018). Land cover and land use change analysis using multi-spatial resolution data and object-based image analysis. Remote Sensing of Environment, 210, 259-268.

Coulter, L. L., Stow, D. A., Tsai, Y. H., Ibanez, N., Shih, H., Kerr, A., ... & Mensah, F. (2016). Classification and assessment of land cover and land use change in southern Ghana using dense stacks of Landsat 7 ETM+ imagery. Remote Sensing of Environment, 184, 396-409.

Stow, D. A., Weeks, J. R., Shih, H., Coulter, L. L., Johnson, H., Tsai, Y. H., ... & Mensah, F. (2016). Inter-regional pattern of urbanization in southern Ghana in the first decade of the new millennium. Applied Geography, 71, 32-43.

Shih, H., Stow, D. A., Weeks, J. R., & Coulter, L. L. (2016). Determining the type and starting time of land cover and land use change in southern Ghana based on discrete analysis of dense Landsat image time series. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(5), 2064-2073.

Toure, S., Stow, D., Shih, H., Coulter, L., Weeks, J., Engstrom, R., & Sandborn, A. (2016). An object-based temporal inversion approach to urban land use change analysis. Remote Sensing Letters, 7(5), 503-512.

Shih, H. (2015). Determining the type and starting time of land cover and land use change in Ghana based on discrete analysis of dense Landsat image time series (Master’s thesis, San Diego State University).

Stow, D. A., Shih, H., & Coulter, L. L. (2014). Discrete classification approach to land cover and land use change identification based on Landsat image time sequences. Remote sensing letters, 5(10), 922-931.

Chang, K., Tian, Y., & Shih, H. (2012). Using multi-temporal and PCA&NDVI to improve the accuracy and integrity of land cover classification, Journal of Geographical Research, (57), 49-60.

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