Priyanka Chaudhary

Priyanka Chaudhary

Computer Vision and Machine Learning Student and Enthusiast

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Pages

Posts

Future Blog Post

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Blog Post number 1

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portfolio

posters

Floodwater-Level Estimation from Social Media Images

In this paper, we have presented a model to predict flood-water level from images gathered from social media platforms in a fully automatized way. The prediction is done using a deep learning framework. More specifically we have build this model on top of the Mask R-CNN architecture. The proposed model performs instance segmentation and at the same time predicts flood level whenever an instance of some specific objects is detected. We further provide a method to combine the multiple object instances level predictions and obtain a single water level prediction for the entire image. The conducted experiments proved the ability of the trained model to effectively predict water level from images within an acceptable error.

projects

Data Mining Lab

In a team of three:

  • During the course, we went through the whole path of data mining from dataset preparation up to meaningful predictions.
  • It included the following steps: dataset search and description, understanding the data and naive introspection, feature construction and selection and prediction and evaluation.
  • We worked on the Mashable dataset from UCI repository. After finding inconsistencies in the dataset we rebuild the dataset ourselves. We did descriptive analysis and text mining on the data and prepared our final features. Our final goal was to predict the number of shares on social media that reflects an articles’s popularity and give recommendations to the author in case the prediction is unpopular.
  • Tools: Python, R

Flood-Water Estimation from Social Media Images

In this work, we extended the work done on my Master thesis project. The paper on the project was published in ISPRS Annals and can be found here Paper. Our paper won the Best Paper Award at ISPRS Geospatial Week 2019 in Semantic Scene Analysis and 3D Reconstruction from Images and Images Sequences track. Award. For our future work, we also want to move from fully-supervised task to weak supervision with learning to rank module. The motivation is to use the ordinal information of flood levels which is ignored when training with regression or classification task. Also, the weak supervision required to train a ranking network is a much simpler annotation task than pixel-wise annotation of images.

Flood-Water Estimation through Semantic Image Interpretation

We propose a method to quantify flood water from images gathered from social media. Quantifying flood height by looking just at images is a difficult task. Therefore, in this work we are using common objects of known dimension which are partially submerged in flood water to quantify flood height. There are various factors which makes this task difficult:

  • images are cluttered due to presence of different classes;
  • objects of different classes present in various scales depending on the distance at which the image was taken;
  • detection of occluded objects as they are partially submerged in flood water. Also, high intra-class variability of instances of objects like buildings or houses and cars.

We model this problem on two levels. For the first part, we train a network to give per image floodwater-level. For second part, we train a model to give more finer classification. For every instance of an object in an image, the model will predict the class of the object and level of flood. Through this project we also contribute a dataset of flood images gleaned from social media.

NLU Project

In a team of two:

  • Implementing a simple LSTM Language model to perform various experiments.
  • Implementing a system that can solve the Story Cloze task. In the Story Cloze Task we were given a four-sentence short story and two alternatives for the last, fifth sentence that ends the story. Our designed system should decide which among those two are correct. The main challenge lies in the fact that the training data consists of five-sentence short stories that include the correct ending but there is no incorrect ending provided. Due to this to use the training data, we need to either generate wrong endings or use another source for training. The validation set consists of correct and incorrect endings but it is much smaller than training dataset to train. We approached this task by using alternate reading comprehension dataset RACE and training on data generated from it.
  • Tools: Python, Tensorflow

publications

Flood-water Level Estimation from Social Media Images

Published in ISPRS Annals, 2019

In the event of a flood, being able to build accurate flood level maps is essential for supporting emergency plan operations. In order to build such maps, it is important to collect observations from the disaster area. Social media platforms can be useful sources of information in this case, as people located in the flood area tend to share text and pictures depicting the current situation. Developing an effective and fully automatized method able to retrieve data from social media and extract useful information in real-time is crucial for a quick and proper response to these catastrophic events. In this paper, we propose a method to quantify flood-water from images gathered from social media. If no prior information about the zone where the picture was taken is available, one possible way to estimate the flood level consists of assessing how much the objects appearing in the image are submerged in water. There are various factors that make this task difficult: i) the precise size of the objects appearing in the image might not be known; ii) flood-water appearing in different zones of the image scene might have different height; iii) objects may be only partially visible as they can be submerged in water. In order to solve these problems, we propose a method that first locates selected classes of objects whose sizes are approximately known, then, it leverages this property to estimate the water level. To prove the validity of this approach, we first build a flood-water image dataset, then we use it to train a deep learning model. We finally show the ability of our trained model to recognize objects and at the same time predict correctly flood-water level. Keywords: Object detection, Deep learning, Image segmentation, Flood estimation, Instance segmentation, Flood detection

Recommended citation: Chaudhary, P., D’Aronco, S., Moy de Vitry, M., Leitão, J. P., and Wegner, J. D.: FLOOD-WATER LEVEL ESTIMATION FROM SOCIAL MEDIA IMAGES, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W5, 5-12, https://doi.org/10.5194/isprs-annals-IV-2-W5-5-2019, 2019. https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W5/5/2019/

Water level prediction from social media images with a multi-task ranking approach

Published in ISPRS Journal of Photogrammetry and Remote Sensing, Volume 167, 2020, Pages 252-262, ISSN 0924-2716, 2020

Floods are among the most frequent and catastrophic natural disasters and affect millions of people worldwide. It is important to create accurate flood maps to plan (offline) and conduct (real-time) flood mitigation and flood rescue operations. Arguably, images collected from social media can provide useful information for that task, which would otherwise be unavailable. We introduce a computer vision system that estimates water depth from social media images taken during flooding events, in order to build flood maps in (near) real-time. We propose a multi-task (deep) learning approach, where a model is trained using both a regression and a pairwise ranking loss. Our approach is motivated by the observation that a main bottleneck for image-based flood level estimation is training data: it is difficult and requires a lot of effort to annotate uncontrolled images with the correct water depth. We demonstrate how to efficiently learn a predictor from a small set of annotated water levels and a larger set of weaker annotations that only indicate in which of two images the water level is higher, and are much easier to obtain. Moreover, we provide a new dataset, named DeepFlood, with 8145 annotated ground-level images, and show that the proposed multi-task approach can predict the water level from a single, crowd-sourced image with ≈11 cm root mean square error.

Recommended citation: @article{CHAUDHARY2020252, title = {Water level prediction from social media images with a multi-task ranking approach}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, volume = {167}, pages = {252-262}, year = {2020}, issn = {0924-2716}, doi = {https://doi.org/10.1016/j.isprsjprs.2020.07.003}, url = {https://www.sciencedirect.com/science/article/pii/S0924271620301842}, author = {P. Chaudhary and S. D’Aronco and J.P. Leitão and K. Schindler and J.D. Wegner}} https://doi.org/10.1016/j.isprsjprs.2020.07.003

Flood Uncertainty Estimation Using Deep Ensembles

Published in Water, 2022

In the event of a flood, being able to build accurate flood level maps is essential for supporting emergency plan operations. In order to build such maps, it is important to collect observations from the disaster area. Social media platforms can be useful sources of information in this case, as people located in the flood area tend to share text and pictures depicting the current situation. Developing an effective and fully automatized method able to retrieve data from social media and extract useful information in real-time is crucial for a quick and proper response to these catastrophic events. In this paper, we propose a method to quantify flood-water from images gathered from social media. If no prior information about the zone where the picture was taken is available, one possible way to estimate the flood level consists of assessing how much the objects appearing in the image are submerged in water. There are various factors that make this task difficult: i) the precise size of the objects appearing in the image might not be known; ii) flood-water appearing in different zones of the image scene might have different height; iii) objects may be only partially visible as they can be submerged in water. In order to solve these problems, we propose a method that first locates selected classes of objects whose sizes are approximately known, then, it leverages this property to estimate the water level. To prove the validity of this approach, we first build a flood-water image dataset, then we use it to train a deep learning model. We finally show the ability of our trained model to recognize objects and at the same time predict correctly flood-water level. Keywords: Object detection, Deep learning, Image segmentation, Flood estimation, Instance segmentation, Flood detection

Recommended citation: Chaudhary P, Leitão JP, Donauer T, D’Aronco S, Perraudin N, Obozinski G, Perez-Cruz F, Schindler K, Wegner JD, Russo S. Flood Uncertainty Estimation Using Deep Ensembles. Water. 2022; 14(19):2980. https://doi.org/10.3390/w14192980 https://www.mdpi.com/2073-4441/14/19/2980

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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