In this paper we investigate the amount of spatial context required for channel attention. To this end we study the popular squeeze-and-excite (SE) block which is a simple and lightweight channel attention mechanism. SE blocks and its numerous variants commonly use global average pooling (GAP) to create a single descriptor for each channel. Here, we empirically analyze the amount of spatial context needed for effective channel attention and find that limited localcontext on the order of seven rows or columns of the original image is sufficient to match the performance of global context. We propose tiled squeeze-and-excite (TSE), which is a framework for building SE-like blocks that employ several descriptors per channel, with each descriptor based on local context only. We further show that TSE is a drop-in replacement for the SE block and can be used in existing SE networks without re-training. This implies that local context descriptors are similar both to each other and to the global context descriptor. Finally, we show that TSE has important practical implications for deployment of SE-networks to dataflow AI accelerators due to their reduced pipeline buffering requirements. For example, using TSE reduces the amount of activation pipeline buffering in EfficientDetD2 by 90% compared to SE (from 50M to 4.77M) without loss of accuracy. Our code and pre-trained models will be publicly available.

Making predictions in a robust way is not easy for nonlinear systems. In this work, a neural network computing framework, i.e., a spatiotemporal convolutional network (STCN), was developed to efficiently and accurately render a multistep-ahead prediction of a time series by employing a spatial-temporal information (STI) transformation. The STCN combines the advantages of both the temporal convolutional network (TCN) and the STI equation, which maps the high-dimensional/spatial data to the future temporal values of a target variable, thus naturally providing the prediction of the target variable. From the observed variables, the STCN also infers the causal factors of the target variable in the sense of Granger causality, which are in turn selected as effective spatial information to improve the prediction robustness. The STCN was successfully applied to both benchmark systems and real-world datasets, all of which show 1 superior and robust performance in multistep-ahead prediction, even when the data were perturbed by noise. From both theoretical and computational viewpoints, the STCN has great potential in practical applications in artificial intelligence (AI) or machine learning fields as a model-free method based only on the observed data, and also opens a new way to explore the observed high-dimensional data in a dynamical manner for machine learning.

Spatial joins allow to augment one spatial dataset with information from another spatial dataset by linking overlapping features. In this post I will provide an example showing how to augment a dataset containing school locations with socioeconomic data of their surrounding statistical region using R and the package sf (Pebesma 2018). This approach has the drawback that the surrounding statistical region doesn't reflect the actual catchment area of the school. I will present an alternative approach where the overlaps of the schools' catchment areas with the statistical regions allow to calculate the weighted average of the socioeconomic statistics. If we have no data about the actual catchment areas of the schools, we may resort to approximating these areas as circular regions or as Voronoi regions around schools.

Digital gigapixel whole slide image (WSI) is widely used in clinical diagnosis, and automated WSI analysis is key for computer-aided diagnosis. Currently, analyzing the integrated descriptor of probabilities or feature maps from massive local patches encoded by ResNet classifier is the main manner for WSI-level prediction. Feature representations of the sparse and tiny lesion cells in cervical slides, however, are still challengeable for the under-promoted upstream encoders, while the unused spatial representations of cervical cells are the available features to supply the semantics analysis. As well as patches sampling with overlap and repetitive processing incur the inefficiency and the unpredictable side effect. This study designs a novel inline connection network (InCNet) by enriching the multi-scale connectivity to build the lightweight model named You Only Look Cytopathology Once (YOLCO) with the additional supervision of spatial information. The proposed model allows the input size enlarged to megapixel that can stitch the WSI without any overlap by the average repeats decreased from $10^3\sim10^4$ to $10^1\sim10^2$ for collecting features and predictions at two scales. Based on Transformer for classifying the integrated multi-scale multi-task features, the experimental results appear $0.872$ AUC score better and $2.51\times$ faster than the best conventional method in WSI classification on multicohort datasets of 2,019 slides from four scanning devices.

"We just know it's the right thing to do for our customers and--I say this from years of doing risk management-- it's good, basic risk management," says Shannon Carroll, director of global environmental sustainability at AT&T. "If all indications are that something is going to happen in the future, it's our responsibility to be prepared for that." Globally, leaders from government, business, and academia see the urgency. When citing risks with the highest impact, those surveyed listed climate action failure and other environmental risks second only to infectious diseases. AT&T is taking action with its Climate Resilience Project, using spatial data analysis and location information to tackle the complex problem of how increasingly powerful storms could affect infrastructure such as cell towers and the telecom's ability to deliver service to its customers. "Spatial analysis is this way of going beyond what we visually see," explains Lauren Bennett, head of spatial analysis and data science at Esri, a geographic information systems (GIS) company.

Human interactions with the physical environment are often mediated through information services, and sometimes depend on them. These human interactions with their environment relate to a range of scales,28 in the scenario here from the "west of the city" to the "back of the store," or beyond the scenario to "the cat is under the sofa." These interactions go far beyond references to places that are recorded in geographic gazetteers,37 both in scale (the place where the cat is) and conceptualization (the place that forms the west of the city29), or that fit to the classical coordinate-based representations of digital maps. And yet, these kinds of services have to use such digital representations of environments, such as digital maps, building information models, knowledge bases, or just text/documents. Also, their abilities to interact are limited to either fusing with the environment,44 or using media such as maps, photos, augmented reality, or voice. These interactions also happen in a vast range of real-world contexts, or in situ, in which conversation partners typically adapt their conversational strategies to their interlocutor, based on mutual information, activities, and the shared situation.2 Verbal information sharing and conversations about places may also be more suitable when visual communication through maps or imagery is inaccessible, distracting, or irrelevant, such as when navigating in a familiar shopping mall.

High-resolution satellite imagery has proven useful for a broad range of tasks, including measurement of global human population, local economic livelihoods, and biodiversity, among many others. Unfortunately, high-resolution imagery is both infrequently collected and expensive to purchase, making it hard to efficiently and effectively scale these downstream tasks over both time and space. We propose a new conditional pixel synthesis model that uses abundant, low-cost, low-resolution imagery to generate accurate high-resolution imagery at locations and times in which it is unavailable. We show that our model attains photo-realistic sample quality and outperforms competing baselines on a key downstream task -- object counting -- particularly in geographic locations where conditions on the ground are changing rapidly.

Outlier detection is a significant area in data mining. It can be either used to pre-process the data prior to an analysis or post the processing phase (before visualization) depending on the effectiveness of the outlier and its importance. Outlier detection extends to several fields such as detection of credit card fraud, network intrusions, machine failure prediction, potential terrorist attacks, and so on. Outliers are those data points with characteristics considerably different. They deviate from the data set causing inconsistencies, noise and anomalies during analysis and result in modification of the original points However, a common misconception is that outliers have to be immediately eliminated or replaced from the data set. Such points could be considered useful if analyzed separately as they could be obtained from a separate mechanism entirely making it important to the research question. This study surveys the different methods of outlier detection for spatial analysis. Spatial data or geospatial data are those that exhibit geographic properties or attributes such as position or areas. An example would be weather data such as precipitation, temperature, wind velocity, and so on collected for a defined region.

Recently, autonomous vehicles and those equipped with an Advanced Driver Assistance System (ADAS) are emerging. They share the road with regular ones operated by human drivers entirely. To ensure guaranteed safety for passengers and other road users, it becomes essential for autonomous vehicles and ADAS to anticipate traffic accidents from natural driving scenes. The dynamic spatial-temporal interaction of the traffic agents is complex, and visual cues for predicting a future accident are embedded deeply in dashcam video data. Therefore, early anticipation of traffic accidents remains a challenge. To this end, the paper presents a dynamic spatial-temporal attention (DSTA) network for early anticipation of traffic accidents from dashcam videos. The proposed DSTA-network learns to select discriminative temporal segments of a video sequence with a module named Dynamic Temporal Attention (DTA). It also learns to focus on the informative spatial regions of frames with another module named Dynamic Spatial Attention (DSA). The spatial-temporal relational features of accidents, along with scene appearance features, are learned jointly with a Gated Recurrent Unit (GRU) network. The experimental evaluation of the DSTA-network on two benchmark datasets confirms that it has exceeded the state-of-the-art performance. A thorough ablation study evaluates the contributions of individual components of the DSTA-network, revealing how the network achieves such performance. Furthermore, this paper proposes a new strategy that fuses the prediction scores from two complementary models and verifies its effectiveness in further boosting the performance of early accident anticipation.

Geospatial data science is a subset of data science that focuses on spatial data and its unique techniques. In this, we are going to perform spatial analysis and trying to find insights from spatial data. In this course, we lay the foundation for a career in Geospatial Data Science. You will get hands-on Geopy, Plotly etc.. the workhorse of Geospatial data science Python libraries. The topics covered in this course widely touch on some of the most used spatial technique in Geospatial data science.