The data science and AI community has gathered around the world to support tackling the climate change problem in different domains. This research aims to work on the air quality through emissions and pollutant concentration data along with vegetation information. Authorities especially in urban cities like London have been very vigilant in monitoring these different aspects of air quality and reliable sources of big data are available in this domain. This study aims to mine and collate this information spread all over the place in different formats into usable knowledge base on which further data analysis and powerful Machine Learning approaches can be built to extract strong evidences useful in building better policies around climate change. Keywords: Data mining and analysis • Data pre-processing • Air quality • Climate change • Urban planning and machine learning • Geographic information systems

Despite the significant advances achieved in Artificial Neural Networks (ANNs), their design process remains notoriously tedious, depending primarily on intuition, experience and trial-and-error. This human-dependent process is often time-consuming and prone to errors. Furthermore, the models are generally bound to their training contexts, with no considerations of changes to their surrounding environments. Continual adaptability and automation of neural networks is of paramount importance to several domains where model accessibility is limited after deployment (e.g IoT devices, selfdriving vehicles, etc). Additionally, even accessible models require frequent maintenance post-deployment to overcome issues such as Concept/Data Drift, which can be cumbersome and restrictive. The current state of the art on adaptive ANNs is still a premature area of research; nevertheless, Neural Architecture Search (NAS), a form of AutoML, and Continual Learning (CL) have recently gained an increasing momentum in the Deep Learning research field, aiming to provide more robust and adaptive ANN development frameworks. This study is the first extensive review on the intersection between AutoML and CL, outlining research directions for the different methods that can facilitate full automation and lifelong plasticity in ANNs.

In the light of Artificial Intelligence aiding modern society in tackling climate change, this research looks at how to detect vegetation from aerial view images using deep learning models. This task is part of a proposed larger framework to build an eco-system to monitor air quality and the related factors like weather, transport, and vegetation, as the number of trees for any urban city in the world. The challenge involves building or adapting the tree recognition models to a new city with minimum or no labeled data. This paper explores self-supervised approaches to this problem and comes up with a system with 0.89 mean average precision on the Google Earth images for Cambridge city.