SIL861: ICTD - environment focus

This is a list of readings we covered in the SIL861 course taught during the 2022 Holi semester. Please write to me if you want access to the lecture videos and all the readings in one place.


Introduction to contemporary challenges (through videos)


Development: Definitions and criticism

Papers on the use of satellite data for poverty mapping and other applications
  • Different remote sensing methods
  • Nightlights
    • Baskaran, T., et al., Election cycles and electricity provision: Evidence from a quasi-experiment with Indian special elections, J. Public Econ. (2015),
    • Anupam Prakash, Avdhesh Kumar Shukla, Chaitali Bhowmick and Robert Carl Michael Beyer Night-time Luminosity: Does it Brighten Understanding of Economic Activity in India?
    • Lee, Y. S. (2018, January). International isolation and regional inequality: Evidence from sanctions on North Korea. Journal of Urban Economics, 34–51.
  • Pixel based methods
    • Bansal, C., Ahlawat, H. O., Jain, M., Prakash, O., Mehta, S. A., Singh, D., ... & Seth, A. (2021, June). IndiaSat: A Pixel-Level Dataset for Land-Cover Classification on Three Satellite Systems-Landsat-7, Landsat-8, and Sentinel-2. In ACM SIGCAS Conference on Computing and Sustainable Societies (pp. 147-155).
    • Bansal, C., Singla, A., Singh, A. K., Ahlawat, H. O., Jain, M., Singh, P., ... & Seth, A. (2020, June). Characterizing the evolution of indian cities using satellite imagery and open street maps. In Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies (pp. 87-96).
    • Kohli, D., Sliuzas, R., & Stein, A. (2016). Urban slum detection using texture and spatial metrics derived from satellite imagery. Journal of spatial science, 61(2), 405-426.
    • Goldblatt, R., Deininger, K., & Hanson, G. (2018). Utilizing publicly available satellite data for urban research: Mapping built-up land cover and land use in Ho Chi Minh City, Vietnam. Development Engineering, 3, 83-99.
  • CNNs and transfer learning
    • Neal Jean, Marshall Burke, Michael Xie, W. Matthew Davis, David B. Lobell, Stefano Ermon. (2016). Combining satellite imagery and machine learning to predict poverty
    • Neal Jean, Marshall Burke, Michael Xie, W. Matthew Davis, David B. Lobell, Stefano Ermon. (2016). Supplementary Materials for Combining satellite imagery and machine learning to predict poverty
  • Interpretable maps
    • Ayush, K., Uzkent, B., Burke, M., Lobell, D., & Ermon, S. (2020). Generating interpretable poverty maps using object detection in satellite images. arXiv preprint arXiv:2002.01612.
  • U-Net based methods
    • Gudžius, P., Kurasova, O., Darulis, V., & Filatovas, E. (2021). Deep learning-based object recognition in multispectral satellite imagery for real-time applications. Machine Vision and Applications, 32(4), 1-14.
    • Zhang, X., Han, L., Han, L., & Zhu, L. (2020). How well do deep learning-based methods for land cover classification and object detection perform on high resolution remote sensing imagery?. Remote Sensing, 12(3), 417.
  • Self-supervised learning
    • Ghosh, Rahul, Xiaowei Jia, Chenxi Lin, Zhenong Jin and Vipin Kumar. “Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping.” ArXiv abs/2108.07323 (2021): n. pag.
  • Land-use change detection
    • Goldblatt, R., Heilmann, K. & Vaizman, Y. (2017). Properties of Satellite Imagery for Measuring Economic Activity at Small Geographies.
    • Faridatul, M. I. & Wu, B. (2018). Automatic Classification of Major Urban Land Covers Based on Novel Spectral Indices. ISPRS International Journal of Geo-Information, 7(12), 453,
    • Robinson, C., Ortiz, A., Ferres, J.M., Anderson, B.R. & Ho, D.E. (2021). Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery. ACM SIGCAS Conference on Computing and Sustainable Societies, 138-146
  • Agricultural yield estimation
    • David B. Lobell, David Thau, Christopher Seifert, Eric Engle, Bertis Little (2015) A scalable satellite-based crop yield mapper
    • Rose Rustowicz, Robin Cheong, Lijing Wang, Stefano Ermon, Marshall Burke, David Lobell (2019) Semantic Segmentation of Crop Type in Africa: A Novel Dataset and Analysis of Deep Learning Methods
    • Rahul Ghosh, Praveen Ravirathinam, Xiaowei Jia, Chenxi Lin, Zheong Jin, Vipin Kumar (2021) Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping
  • Statistical techniques for spatio-temporal data analysis
    • Shashi Shekhar, Ranga Raju Vatsavai, Mete Celik (2008) Spatial and Spatiotemporal Data Mining: Recent Advances
    • David J. Peters (2012) Income Inequality across Micro and Meso Geographic Scales in the Midwestern United States, 1979–20091
    • Somik Vinay Lall and Sanjoy Chakravorty (2005) Industrial Location and Spatial Inequality: Theory and Evidence from India


Carbon related topics

  • Carbon cycle, sequestration estimation
    • Zhiliang Zhu (editor), Brian Bergamaschi, Richard Bernknopf, David Clow, Dennis Dye, Stephen Faulkner, William Forney, Robert Gleason, Todd Hawbaker, Jinxun Liu, Shuguang Liu, Stephen Prisley, Bradley Reed, Matthew Reeves, Matthew Rollins, Benjamin Sleeter, Terry Sohl, Sarah Stackpoole, Stephen Stehman, Robert Striegl, Anne Wein :- A Method for Assessing Carbon Stocks, Carbon Sequestration, and Greenhouse- Gas Fluxes in Ecosystems of the United States Under Present Conditions and Future Scenarios
  • Site selection
    • NETL (National Energy Technology Laboratory), 2017, Best Practices for Site Screening, Site Selection, and Site Characterization for Geologic Storage Projects.
  • Value and limits of nature based solutions for climate change
    • Seddon N, Chausson A,Berry P, Girardin CAJ, Smith A, Turner B. 2020. Understanding the value and limits ofnature-based solutions to climate change andother global challenges.Phil. Trans. R. Soc. B375: 20190120.
  • Carbon credit issues
    • West, T. A., Börner, J., Sills, E. O., & Kontoleon, A. (2020). Overstated carbon emission reductions from voluntary REDD+ projects in the Brazilian Amazon. Proceedings of the National Academy of Sciences, 117(39), 24188-24194.
  • Problem with carbon offsets
    • Badgley, G., Freeman, J., Hamman, J. J., Haya, B., Trugman, A. T., Anderegg, W. R., & Cullenward, D. (2021). Systematic over-crediting in California's forest carbon offsets program. bioRxiv.
  • Carbon flux estimation methods
    • Harris, Nancy & Gibbs, David & Baccini, A. & Birdsey, Richard & de Bruin, Sytze & Farina, Mary & Fatoyinbo, Lola & Hansen, Matthew & Herold, Martin & Houghton, Richard & Potapov, Peter & Requena Suarez, Daniela & Román-Cuesta, Rosa Maria & Saatchi, Sassan & Slay, Christy & Turubanova, Svetlana & Tyukavina, Alexandra. (2021). Global maps of twenty-first century forest carbon fluxes. Nature Climate Change. 11. 1-7. 10.1038/s41558-020-00976-6.
  • Carbon sequestration estimation methods
    • Iizuka, Kotaro & Tateishi, Ryutaro. (2015). Estimation of CO2 Sequestration by the Forests in Japan by Discriminating Precise Tree Age Category using Remote Sensing Techniques. Remote Sensing. 7. 15082-15113. 10.3390/rs71115082.
  • MGNREGA assets - carbon sequestration estimation model
    • Ravindranath, N.H. & Murthy, Indu K. (2021) mitigation co-benfits of carbon sequestration from MGNREGS in India. PloS ONE, Vol. 16. No.5.
    • Ravindranath, N.H. & Murthy, Indu K. (2018) Estimation of carbon sequestration under MGNREGA: Achievement and Potential in India. Department for Interational Development
  • Climate change communication
    • Fischer, Harry W. (2019) Policy innovations for pro-poor climate support: social protection, small-scale infrastructure, and active citizenship under India's MGNREGA. Climate and Development, Vol. 12, No. 8, pp 689-702.
    • Khatibi, F.s & Dedekorkut-Howes, Aysin & Howes, Michael & Torabi, Elnaz. (2021). Can public awareness, knowledge and engagement improve climate change adaptation policies?. Discover Sustainability. 2. 10.1007/s43621-021-00024-z.


Forest related topics

  • Forest cover change estimation
    • Mitchard, E.T.A (2016) A Review of Earth Observation Methods for Detecting and Measuring Forest Change in the Tropics. Ecometrica. Edinburgh, UK.
    • Global forest watch
  • Deforestation alerts
    • Buras, A., Rammig, A., & Zang, C. S. (2021). The European Forest Condition Monitor: Using Remotely Sensed Forest Greenness to Identify Hot Spots of Forest Decline. Frontiers in plant science, 12, 689220-689220.
    • Ortega Adarme, M., Queiroz Feitosa, R., Nigri Happ, P., Aparecido De Almeida, C., & Rodrigues Gomes, A. (2020). Evaluation of deep learning techniques for deforestation detection in the Brazilian Amazon and cerrado biomes from remote sensing imagery. Remote Sensing, 12(6), 910.
    • Pickering, J., Tyukavina, A., Khan, A., Potapov, P., Adusei, B., Hansen, M. C., & Lima, A. (2021). Using multi-resolution satellite data to quantify land dynamics: applications of PlanetScope imagery for cropland and tree-cover loss area estimation. Remote Sensing, 13(11), 2191.
  • Integration of GEDI and Landsat for canopy height assessment
    • Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M. C., Kommareddy, A., ... & Hofton, M. (2021). Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sensing of Environment, 253, 112165.
    • Dubayah, R., Blair, J. B., Goetz, S., Fatoyinbo, L., Hansen, M., Healey, S., ... & Silva, C. (2020). The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography. Science of remote sensing, 1, 100002.
  • Forest management in India
    • Nayak, Prateep. (2003). Community-based forest management in India: The significance of tenure. Forests, Trees and Livelihoods. 13. 10.1080/14728028.2003.9752451.
  • Community mapping
    • Zafar, Tanjia & Ding, Wenguang & Rahimoon, Shaker & Murtaza, Khan & Hao, Chen & He, Li. (2021). Forest cover and land use map of the Chunati Wildlife Sanctuary based on participatory mapping and satellite images: Insight into Chunati beat. Land Use Policy. 103. 105193. 10.1016/j.landusepol.2020.105193.


Water related topics

  • Remote sensing for water resources
    • Kumar, D Nagesh & Tv, Reshmidevi. (2013). Remote Sensing Applications in Water Resources. Journal of the Indian Institute of Science. 93. 163-188.
    • Huang, Chang & Chen, Yun & Zhang, Shiqiang & Wu, Jianping. (2018). Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review. Reviews of Geophysics. 56. 10.1029/2018RG000598.
  • Water storage estimation
    • Tortini, Riccardo & Noujdina, Nina & Yeo, Samantha & Ricko, Martina & Birkett, Charon & Khandelwal, Ankush & Kumar, Vipin & Marlier, Miriam & Lettenmaier, Dennis. (2020). Satellite-based remote sensing data set of global surface water storage change from 1992 to 2018. Earth System Science Data. 12. 1141-1151. 10.5194/essd-12-1141-2020.
  • Soil moisture and agricultural yield
    • Babaeian, E., Sadeghi, M., Jones, S. B., Montzka, C., Vereecken, H., & Tuller, M. (2019). Ground, proximal, and satellite remote sensing of soil moisture. Reviews of Geophysics, 57(2), 530-616.
    • Holzman, M. E., Rivas, R., & Piccolo, M. C. (2014). Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index. International Journal of Applied Earth Observation and Geoinformation, 28, 181-192.


Crowd-sourcing and community based planning

  • Natural resource management
    • Ritu Bharadwaj, Simon Addison and Mohan Reddy (2021), Climate Resilience Information System and Planning Tool for Mahatma Gandhi National Rural Employment Guarantee Scheme: the CRISP-M tool. IIED working paper, IIED, London.
  • Crowdsourced data collection and cleaning
    • Mohamad Dolatshahy, Mathew Teohy, Jiannan Wangy, Jian Pei. (2018). Cleaning Crowdsourced Labels Using Oracles for Statistical Classification. SFU summit,
    • Zheng, F., Tao, R., Maier, H. R., See, L., Savic, D., Zhang, T., ... & Popescu, I. (2018). Crowdsourcing methods for data collection in geophysics: State of the art, issues, and future directions. Reviews of Geophysics, 56(4), 698-740.
    • Jacobs, C. (2016). Data quality in crowdsourcing for biodiversity research: issues and examples. European handbook of crowdsourced geographic information, 75.
    • Guiteras, Raymond and Jina, Amir and Mobarak, A Mushfiq.(2015).Satellites, self-reports, and submersion: exposure to floods in Bangladesh.American Economic Review, Vol 105.
    • Blumenstock, Joshua E and Keleher, Niall. (2015). The price is right? statistical evaluation of a crowd-sourced market information system in liberia.Proceedings of the 2015 Annual Symposium on Computing for Development.


Agricultural commodities

  • Context of agricultural markets
    • White, B. H. (2013). West Bengal's rural commercial capital. Taylor; Francis
    • Kapur, D., Krishnamurthy, M., Witsoe, J., Tirupati, D., Mishra, P. K., & CASI. (2014). “Understanding Mandis: Market Towns and the Dynamics of India’s Rural and Urban Transformations” Devesh Kapur & Mekhala Krishnamurthy. Center for the Advanced Study of India (CASI).
    • Chatterjee, S. & Kapur, D. (2016). “Understanding Price Variation in Agricultural Commodities in India: MSP, Government Procurement, and Agriculture Markets” Shoumitro Chatterjee & Devesh Kapur
  • Time series analysis methods: ARIMA, SARIMA, VAR, LSTM, TCN
  • Understanding influence of external shocks
    • Gene P. K. Wu and Keith C. C. Chan. 2020. Discovery of Spatio-Temporal Patterns in Multivariate Spatial Time Series. ACM/IMS Trans. Data Sci. 1, 2, Article 11 (May 2020), 22 pages. DOI:
    • Nidhi Aggarwal and Sudha Narayanan - The impact of India’s demonetization ondomestic agricultural trade


News media related indicators

  • Various case studies
    • Chakraborty, S. Venkataraman, A. Jagabathula, S. & Subramanian, L. (2016). Predicting Socio-Economic Indicators using News Events. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16). Association for Computing Machinery, NY, USA, 1455–1464,
    • Dolan Antenucci & Michael Cafarella & Margaret Levenstein & Christopher Ré & Matthew D. Shapiro, 2014. "Using Social Media to Measure Labor Market Flows," NBER Working Papers 20010, National Bureau of Economic Research, Inc.
    • Obst, D. Ghattas, B. Claudel, S. Cugliari, J. & Oppenheim, G. (2019). Textual Data for Time Series Forecasting. arXiv. Preprint.


Cooperatives and commons

  • Cooperatives
    • Lampinen, Airi & Mcgregor, Moira & Comber, Rob & Brown, Barry. (2018). Member-Owned Alternatives: Exploring Participatory Forms of Organising with Cooperatives. Proceedings of the ACM on Human-Computer Interaction. 2. 1-19. 10.1145/3274369.
    • Rosemary Steup, Arvind Santhanam, Marisa Logan, Lynn Dombrowski, and Norman Makoto Su. 2018. Growing Tiny Publics: Small Farmers’ Social Movement Strategies. Proc. ACM Hum.-Comput. Interact. 2, CSCW, Article 165 (November 2018), 24 pages."
  • Commons in communism
  • Contributive justice


Design methods

  • Participatory design, user centered design, ethnographic design, communitization
  • Role of incentives
  • Case studies
    • Koradia, Z., Aggarwal, P., Seth, A., & Luthra, G. (2013, January). Gurgaon idol: A singing competition over community radio and IVRS. In Proceedings of the 3rd ACM Symposium on Computing for Development (pp. 1-10).
    • Wong-Villacres, Marisol and DiSalvo, Carl and Kumar, Neha and DiSalvo, Betsy. Culture in Action: Unpacking Capacities to Inform Assets-Based Design. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems.


Evaluation methods

  • Theory of change
  • Impact evaluation methods: Difference in differences, propensity score, regression discontinuity
  • Thematic analysis
  • Dell, N., Vaidyanathan, V., Medhi, I. Cutrell, E. & Thies, W. (2012). “Yours is Better!” Participant Response Bias in HCI. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '12). Association for Computing Machinery, New York, NY, USA, 1321–1330,


Deployment complexities

  • Various case studies
    • Rajesh Veeraraghavan. (2013). Dealing with the Digital Panopticon: The Use and Subversion of ICT in an Indian Bureaucracy
    • Savita Bailur, Silvia Masiero. The Complex Position of the Intermediary in Telecenters and Community Multimedia Centers. Published 9 March 2012.
    • Steyn, J. (2016). A critique of the claims about mobile phones and Kerala fisherman: The importance of the context of complex social systems. The Electronic Journal of Information Systems in Developing Countries, 74(1), 1-31.
    • Kumar N. Facebook for self-empowerment? A study of Facebook adoption in urban India. New Media & Society. 2014;16(7):1122-1137. doi:10.1177/1461444814543999


Data related issues

  • Various case studies
    • Cardullo, Paolo & Kitchin, Rob. (2017). Being a 'citizen' in the smart city: Up and down the scaffold of smart citizen participation.
    • Kitchin, Rob & Cardullo, Paolo & Feliciantonio, Cesare. (2018). Citizenship, Justice and the Right to the Smart City.
    • Richard Heeks, Satyarupa Shekhar (2018) An applied data justice framework: Analysing datafication and marginalised communities in cities of the global south.
    • Richard Heeks,Vanya Rakesh,Ritam Sengupta, Sumandro Chattapadhyay, Christopher Foster (2019) Datafication, value and power in developing countries: Big data in two Indian public service organizations.
    • Catherine Sutherland, Bahle Mazeka,Sibongile Buthelezi,Duduzile Khumalo and Patrick Martel (2019) Making Informal Settlements ‘Visible’ Through Datafication : A case study of Quarry Road West Informal Settlement, Durban, South Africa
    • Bijal Brahmbhatt, Siraz Hiran, Neha Lal, Bhumika Chauhan (2019) Urban Slums in a Datafying Milieu: Challenges for Data Driven Research Practice
    • Wolff, A., Gooch, D., Cavero Montaner, J.J, Rashid, U. & Kortuem, G. (2016). Creating an understanding of data literacy for a data-driven society. The Journal of Community Informatics, 12(3), 9-26.
    • Albornoz, D., Reilly, K. & Flores, M. (2019).  Community-Based Data Justice: A Model for Data Collection in Informal Urban Settlements. Development Informatics Working Paper no. 82.