Crop Identification and Yield Estimation using SAR Data

Accurate crop identification and yield estimation are crucial for policymakers to develop effective agricultural policies, allocate resources efficiently, and support farmers in adopting suitable technologies. However, optical remote sensing methods, commonly used for crop identification and yield estimation, face challenges due to cloud cover and adverse weather conditions. King et al. (2013) [4] estimated that approximately 67 percent of the Earth’s surface is often obscured by clouds, making it difficult to obtain high-quality optical remote sensing data. Additionally, humid and semi-humid climate zones with abundant water sources pose further challenges for remote sensing in agriculture. To overcome these limitations, this project aims to utilize Synthetic Aperture Radar (SAR) data for crop identification and yield estimation. It enables continuous data collection regardless of light and weather conditions by using microwaves that can penetrate clouds. As SAR is sensitive to both the dielectric and geometrical characteristics of plants, it captures information below the vegetation canopy cover and provides insights into crop structure and health. Furthermore, SAR provides flexibility in imaging parameters such as incident angles and polarization configurations, facilitating the extraction of diverse information about agricultural landscapes.

Related Works:

  1. D. Suchi, A. Menon, A. Malik, J. Hu and J. Gao, Crop Identification Based on Remote Sensing Data using Machine Learning Approaches for Fresno County, California, 2021 IEEE Seventh International Conference on Big Data Computing Service and Applications (BigDataService), Oxford, United Kingdom, 2021, pp. 115-124, doi: 10.1109/BigDataService52369.2021.00019.
  2. Liu, C., Chen, Z., Shao, Y., Chen, J., Hasi, T., & Pan, H. (2019). Research advances of SAR remote sensing for agriculture applications: A review. Journal of Integrative Agriculture, 18(3), 506-525.
  3.   J. Singh, U. Devi, J. Hazra and S. Kalyanaraman, Crop-Identification Using Sentinel-1 and Sentinel-2 Data for Indian Region, IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 2018, pp. 5312-5314, doi: 10.1109/IGARSS.2018.8517356.
  4.   King, M. D., Platnick, S., Menzel, W. P., Ackerman, S. A., & Hubanks, P. A. (2013). Spatial and Temporal Distribution of Clouds Observed by MODIS Onboard the Terra and Aqua Satellites. IEEE Transactions on Geoscience and Remote Sensing, 51(7), 3826–3852. doi:10.1109/tgrs.2012.2227333

Test-Time Domain Adaptation for Urban Categorization from Satellite Images

The urban environment is a complex system comprising various elements such as buildings, roads, vegetation, and water bodies. Classifying urban cities from satellite images or images captured by UAVs is an important task for urban planning, disaster management, and environmental monitoring. Urban environments differ across various cities of the world, and existing models for urban classification struggle to adapt to these changes. This project aims to develop an adaptive model for classifying urban cities from satellite images. This model will have the capability to generalize across different urban environments and adapt to the changing urban environment in real time. The model will be trained on a large dataset of satellite images of urban cities from different parts of the world. In inference or test time, the parameters of the model will be updated based on the changes in the different urban environments it has been deployed. This work will be built on recent work on Test time domain adaptation methods and our earlier research on the categorization of urban buildup [1], land usage, and land cover [2,3].

Related Works:

  1. Cheng, Q.; Zaber, M.; Rahman, A.M.; Zhang, H.; Guo, Z.; Okabe, A.; Shibasaki, R. Understanding the Urban Environment from Satellite Images with New Classification Method—Focusing on Formality and Informality. Sustainability 2022, 14, 4336.
  2. Rahman, A.K.M.M.; Zaber, M.; Cheng, Q.; Nayem, A.B.S.; Sarker, A.; Paul, O.; Shibasaki, R. Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World. Sensors 2021, 21, 7469.
  3. Niloy, Fahim Faisal, et al. “Attention toward neighbors: A context aware framework for high resolution image segmentation.” 2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021. 

LULC analysis of Bangladesh using Deep Learning

Land use land cover (LULC) is defined how the land is used by humans or what the surface is covered with. Specifically, land use means how a particular land is being used for; for example, farmland, built-up etc. Land Cover refers to what the surface is covered with; it can be river, forest, mountains etc. Thus, LULC information and LULC change over a period of time obtained from satellite data can be used to analyze the geography, socio-economic condition, poverty estimation, urban planning, building categorization etc. In this project we aim to obtain accurate LULC data from satellite images using deep learning methods. Deep learning based methods have been proved to be more effective to extract LULC information compared to traditional classifiers that are still used in GIS systems for this purpose. However, in order to train deep learning methods on satellite images we need to have a good amount of images annotated with ground-truth. For the LULC segmentation task we need pixel by pixel LULC class annotation for the satellite images. The goal of this project is to produce good quality LULC annotation data pixel by pixel for Dhaka city and surrounding area. With a sufficiently trained segmentation model, we can employ this model to perform LULC over the entire Bangladesh without any human effort. Moreover, this project targets to develop segmentation models that can outperform the existing ones by a significant margin.

Understanding the Urban Environment from Satellite Data

This project is a collaboration between CCDS and the Center for Spatial Information Science, The University of Tokyo, Japan. Based on easily accessible data from Google Earth, this work develops and proposes a new urban environment classification method focusing on formality and informality. The method developed in this work divides the urban environment into 16 categories under four classes. Then it is used to draw the urban environment classes maps of the following emerging cities: Nairobi in Kenya, Mumbai in India, Guangzhou in China, Jakarta in Indonesia, Cairo in Egypt, and Lima in Chile. The characteristics of the different urban environments and the differences between the same class in different cities are investigated. The paper also demonstrate the agility of the proposed method by showing how this classification method can be easily augmented with other data such as population per square kilometer to aid the decision-making process.